We have an exciting and rewarding opportunity for you to take your software engineering career to the next level. As a Senior Lead Software Engineer at JPMorgan Chase within the Commercial & Investment Bank's Markets Research Technology team, you serve as a seasoned member of an agile team to design and deliver trusted market-leading technology products in a secure, stable, and scalable way. You are responsible for carrying out critical technology solutions across multiple technical areas within various business functions in support of the firm's business objectives. You will work on challenging Cloud-native data, backend engineering and AIML engineering, helping us industrialize AI/ML models at production scale. This role is a technical hands on engineering role. Experience with data science/ML modeling is advantageous but not essential to this role. Job responsibilities Executes software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems Creates secure and high-quality production code and maintains algorithms that run synchronously with appropriate systems Produces architecture and design artifacts for complex applications while being accountable for ensuring design constraints are met by software code development Builds engineering stack required for Data and AIML products, including data engineering, backend engineering, Cloud infra DevOps and MLOps Designs and implements data engineering solutions, leveraging modern big data technologies Contributes to software engineering communities of practice and events that explore new and emerging technologies Embraces a passion for learning, problem-solving, creative thinking and a can-do attitude. Required qualifications, capabilities, and skills Formal training or certification onsoftware engineeringconcepts and proficient applied experience Hands on practical experience in system design, application development, testing, and operational stability Proficient in coding in one or more languages- Python Experience in developing, debugging, and maintaining code in a large corporate environment with one or more modern programming languages and database querying languages Overall knowledge of the Software Development Life Cycle Proven track record in system design, architecting and developing microservices, distributed systems and data-intensive applications Experience with Cloud services, Infrastructure as Code, containerized application development, big data and modern data engineering technologies Practical experience developing Production scale Cloud-native data engineering solutions in commercial environments Familiarity with Cloud Data engineering services (e.g., ETL, Glue, S3, Athena) and MLOps stack Ability to convey design choices and results clearly and communicate effectively to stakeholders of various backgrounds Preferred qualifications, capabilities, and skills Experience with data, AWS and AIML engineering in commercial settings, preferably in financial sector Experience working on recommendation systems, LLM applications or other AI/ML systems Practical experience with Kubernetes, EKS, Docker, MLOps Prior exposure to LLMs, RAG, Knowledge Graph Technologies, OpenSearch and vector databases Prior experience collaborating with data scientists
21/06/2026
Full time
We have an exciting and rewarding opportunity for you to take your software engineering career to the next level. As a Senior Lead Software Engineer at JPMorgan Chase within the Commercial & Investment Bank's Markets Research Technology team, you serve as a seasoned member of an agile team to design and deliver trusted market-leading technology products in a secure, stable, and scalable way. You are responsible for carrying out critical technology solutions across multiple technical areas within various business functions in support of the firm's business objectives. You will work on challenging Cloud-native data, backend engineering and AIML engineering, helping us industrialize AI/ML models at production scale. This role is a technical hands on engineering role. Experience with data science/ML modeling is advantageous but not essential to this role. Job responsibilities Executes software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems Creates secure and high-quality production code and maintains algorithms that run synchronously with appropriate systems Produces architecture and design artifacts for complex applications while being accountable for ensuring design constraints are met by software code development Builds engineering stack required for Data and AIML products, including data engineering, backend engineering, Cloud infra DevOps and MLOps Designs and implements data engineering solutions, leveraging modern big data technologies Contributes to software engineering communities of practice and events that explore new and emerging technologies Embraces a passion for learning, problem-solving, creative thinking and a can-do attitude. Required qualifications, capabilities, and skills Formal training or certification onsoftware engineeringconcepts and proficient applied experience Hands on practical experience in system design, application development, testing, and operational stability Proficient in coding in one or more languages- Python Experience in developing, debugging, and maintaining code in a large corporate environment with one or more modern programming languages and database querying languages Overall knowledge of the Software Development Life Cycle Proven track record in system design, architecting and developing microservices, distributed systems and data-intensive applications Experience with Cloud services, Infrastructure as Code, containerized application development, big data and modern data engineering technologies Practical experience developing Production scale Cloud-native data engineering solutions in commercial environments Familiarity with Cloud Data engineering services (e.g., ETL, Glue, S3, Athena) and MLOps stack Ability to convey design choices and results clearly and communicate effectively to stakeholders of various backgrounds Preferred qualifications, capabilities, and skills Experience with data, AWS and AIML engineering in commercial settings, preferably in financial sector Experience working on recommendation systems, LLM applications or other AI/ML systems Practical experience with Kubernetes, EKS, Docker, MLOps Prior exposure to LLMs, RAG, Knowledge Graph Technologies, OpenSearch and vector databases Prior experience collaborating with data scientists
J.P.MorganChase is seeking a Lead Software Engineer with expertise in AWS and Python, and a passion for Machine Learning, to help engineer and deploy innovative ML solutions into production. You will collaborate with the Applied AI/ML group and technology teams across the firm, contributing to both new and ongoing projects. In this role, you will work alongside Data Scientists to build cloud-based frameworks for hosting machine learning models, providing software engineering expertise throughout the model development lifecycle. You will leverage both internal and external cloud platforms, utilizing proprietary and open-source tools to ensure models meet SDLC standards, are production-ready, and can be deployed efficiently. The position requires close interaction with platform developers, engineering communities, and the integration of existing and new technologies. Job Responsibilities Develop and maintain high-quality, secure applications using Python and AWS Create architecture and design deliverables, lead design and architecture reviews, promote best practice Integrate AIML solutions into complex, domain-specific operations processing systems Lead code reviews, design discussions, and agile planning sessions Collaborate with SRE and production monitoring teams to ensure system reliability and performance Contribute to software engineering communities of practice and technology events Embrace continuous learning, creative problem-solving, and a can-do attitude Required Qualifications, Capabilities, and Skills Bachelor's degree or higher in Computer Science, Engineering, or a related field, or equivalent formal training/certification Proven hands-on experience in Python application development Proven hands-on experience developing, debugging and maintaining production applications Solid understanding of software development best practices, including version control, testing, and CI/CD Strong problem-solving, communication, and collaboration skills, with the ability to convey design choices and communicate effectively with stakeholders Experience working on AIML systems and/or prior experience collaborating with data scientists Track record of designing, building, and delivering maintainable, extensible applications into production environments Preferred Qualifications, Capabilities, and Skills Experience with Cloud services, Infrastructure as Code (IaC, Terraform) and containerized application development Familiarity with data storage systems (e.g., Postgres, OpenSearch) and AWS services such as S3, SageMaker, and Bedrock Practical experience with Kubernetes, EKS, Docker, Kafka, MLOps, Large Language Model Operations (LLMOps), Event Driven Systems. We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants' and employees' religious practices and beliefs, as well as mental health or physical disability needs. Visit FAQs for more information about requesting an accommodation.
21/06/2026
Full time
J.P.MorganChase is seeking a Lead Software Engineer with expertise in AWS and Python, and a passion for Machine Learning, to help engineer and deploy innovative ML solutions into production. You will collaborate with the Applied AI/ML group and technology teams across the firm, contributing to both new and ongoing projects. In this role, you will work alongside Data Scientists to build cloud-based frameworks for hosting machine learning models, providing software engineering expertise throughout the model development lifecycle. You will leverage both internal and external cloud platforms, utilizing proprietary and open-source tools to ensure models meet SDLC standards, are production-ready, and can be deployed efficiently. The position requires close interaction with platform developers, engineering communities, and the integration of existing and new technologies. Job Responsibilities Develop and maintain high-quality, secure applications using Python and AWS Create architecture and design deliverables, lead design and architecture reviews, promote best practice Integrate AIML solutions into complex, domain-specific operations processing systems Lead code reviews, design discussions, and agile planning sessions Collaborate with SRE and production monitoring teams to ensure system reliability and performance Contribute to software engineering communities of practice and technology events Embrace continuous learning, creative problem-solving, and a can-do attitude Required Qualifications, Capabilities, and Skills Bachelor's degree or higher in Computer Science, Engineering, or a related field, or equivalent formal training/certification Proven hands-on experience in Python application development Proven hands-on experience developing, debugging and maintaining production applications Solid understanding of software development best practices, including version control, testing, and CI/CD Strong problem-solving, communication, and collaboration skills, with the ability to convey design choices and communicate effectively with stakeholders Experience working on AIML systems and/or prior experience collaborating with data scientists Track record of designing, building, and delivering maintainable, extensible applications into production environments Preferred Qualifications, Capabilities, and Skills Experience with Cloud services, Infrastructure as Code (IaC, Terraform) and containerized application development Familiarity with data storage systems (e.g., Postgres, OpenSearch) and AWS services such as S3, SageMaker, and Bedrock Practical experience with Kubernetes, EKS, Docker, Kafka, MLOps, Large Language Model Operations (LLMOps), Event Driven Systems. We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants' and employees' religious practices and beliefs, as well as mental health or physical disability needs. Visit FAQs for more information about requesting an accommodation.
Job Overview We have an exciting and rewarding opportunity for you to take your software engineering career to the next level. As a Senior Lead Software Engineer at JPMorgan Chase within the Commercial & Investment Bank's Markets Research Technology team, you serve as a seasoned member of an agile team to design and deliver trusted market leading technology products in a secure, stable, and scalable way. You are responsible for carrying out critical technology solutions across multiple technical areas within various business functions in support of the firm's business objectives. You will work on challenging Cloud native data, backend engineering, and AIML engineering, helping us industrialize AI/ML models at production scale. This role is a technical hands on Engineering role. Experience with data science/ML modeling is advantageous but not essential to this role. Job Responsibilities Executes software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems Creates secure and high quality production code and maintains algorithms that run synchronously with appropriate systems Produces architecture and design artifacts for complex applications while being accountable for ensuring design constraints are met by software code development Builds engineering stack required for Data and AIML products, including data engineering, backend engineering, Cloud infra DevOps and MLOps Designs and implements data engineering solutions, leveraging modern big data technologies Drives adoption and governance of approved AI assisted engineering practices across teams to improve code quality, delivery speed, and operational outcomes (e.g., AI assisted code review/refactoring, test acceleration, release readiness, incident/root cause analysis), while establishing measurable validation standards (secure coding, peer review, automated testing) and promoting reuse of proven patterns and automation within the SDLC/TLM toolchain. Applies knowledge of tools within the Software Development Life Cycle toolchain, including approved AI assisted development and automation capabilities, to improve the value realized by automation at scale. Contributes to software engineering communities of practice and events that explore new and emerging technologies Embraces a passion for learning, problem solving, creative thinking and a can do attitude. Required Qualifications, Capabilities, and Skills Formal training or certification on software engineering concepts and proficient applied experience Hands on practical experience in system design, application development, testing, and operational stability Proficient in coding in one or more languages - Python Experience in developing, debugging, and maintaining code in a large corporate environment with one or more modern programming languages and database querying languages. Overall knowledge of the Software Development Life Cycle Proven track record in system design, architecting and developing microservices, distributed systems and data intensive applications Experience with Cloud services, Infrastructure as Code, containerized application development, big data and modern data engineering technologies Practical experience developing production scale Cloud native data engineering solutions in commercial environments Familiarity with Cloud Data engineering services (e.g., ETL, Glue, S3, Athena) and MLOps stack Demonstrated experience leading effective use of enterprise authorized AI assisted software development tools within the work environment (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching senior engineers/leads on compliant usage patterns and controls. Ability to convey design choices and results clearly and communicate effectively to stakeholders of various backgrounds Preferred Qualifications, Capabilities, and Skills Experience with data, AWS and AIML engineering in commercial settings, preferably in the financial sector Experience working on recommendation systems, LLM applications or other AI/ML systems Practical experience with Kubernetes, EKS, Docker, MLOps Prior exposure to LLMs, RAG, Knowledge Graph Technologies, OpenSearch and vector databases Prior experience collaborating with data scientists Equal Employment Opportunity We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants' and employees' religious practices and beliefs, as well as mental health or physical disability needs.
16/06/2026
Full time
Job Overview We have an exciting and rewarding opportunity for you to take your software engineering career to the next level. As a Senior Lead Software Engineer at JPMorgan Chase within the Commercial & Investment Bank's Markets Research Technology team, you serve as a seasoned member of an agile team to design and deliver trusted market leading technology products in a secure, stable, and scalable way. You are responsible for carrying out critical technology solutions across multiple technical areas within various business functions in support of the firm's business objectives. You will work on challenging Cloud native data, backend engineering, and AIML engineering, helping us industrialize AI/ML models at production scale. This role is a technical hands on Engineering role. Experience with data science/ML modeling is advantageous but not essential to this role. Job Responsibilities Executes software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems Creates secure and high quality production code and maintains algorithms that run synchronously with appropriate systems Produces architecture and design artifacts for complex applications while being accountable for ensuring design constraints are met by software code development Builds engineering stack required for Data and AIML products, including data engineering, backend engineering, Cloud infra DevOps and MLOps Designs and implements data engineering solutions, leveraging modern big data technologies Drives adoption and governance of approved AI assisted engineering practices across teams to improve code quality, delivery speed, and operational outcomes (e.g., AI assisted code review/refactoring, test acceleration, release readiness, incident/root cause analysis), while establishing measurable validation standards (secure coding, peer review, automated testing) and promoting reuse of proven patterns and automation within the SDLC/TLM toolchain. Applies knowledge of tools within the Software Development Life Cycle toolchain, including approved AI assisted development and automation capabilities, to improve the value realized by automation at scale. Contributes to software engineering communities of practice and events that explore new and emerging technologies Embraces a passion for learning, problem solving, creative thinking and a can do attitude. Required Qualifications, Capabilities, and Skills Formal training or certification on software engineering concepts and proficient applied experience Hands on practical experience in system design, application development, testing, and operational stability Proficient in coding in one or more languages - Python Experience in developing, debugging, and maintaining code in a large corporate environment with one or more modern programming languages and database querying languages. Overall knowledge of the Software Development Life Cycle Proven track record in system design, architecting and developing microservices, distributed systems and data intensive applications Experience with Cloud services, Infrastructure as Code, containerized application development, big data and modern data engineering technologies Practical experience developing production scale Cloud native data engineering solutions in commercial environments Familiarity with Cloud Data engineering services (e.g., ETL, Glue, S3, Athena) and MLOps stack Demonstrated experience leading effective use of enterprise authorized AI assisted software development tools within the work environment (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching senior engineers/leads on compliant usage patterns and controls. Ability to convey design choices and results clearly and communicate effectively to stakeholders of various backgrounds Preferred Qualifications, Capabilities, and Skills Experience with data, AWS and AIML engineering in commercial settings, preferably in the financial sector Experience working on recommendation systems, LLM applications or other AI/ML systems Practical experience with Kubernetes, EKS, Docker, MLOps Prior exposure to LLMs, RAG, Knowledge Graph Technologies, OpenSearch and vector databases Prior experience collaborating with data scientists Equal Employment Opportunity We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants' and employees' religious practices and beliefs, as well as mental health or physical disability needs.
We have an exciting and rewarding opportunity for you to take your software engineering career to the next level. As a Senior Lead Software Engineer at JPMorgan Chase within the Commercial & Investment Bank's Markets Research Technology team, you serve as a seasoned member of an agile team to design and deliver trusted market leading technology products in a secure, stable, and scalable way. You are responsible for carrying out critical technology solutions across multiple technical areas within various business functions in support of the firm's business objectives. You will work on challenging Cloud native data, backend engineering and AIML engineering, helping us industrialize AI/ML models at Production scale. This role is a technical hands on Engineering role. Experience with data science/ML modeling is advantageous but not essential to this role. Job responsibilities Executes software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems Creates secure and high quality production code and maintains algorithms that run synchronously with appropriate systems Produces architecture and design artifacts for complex applications while being accountable for ensuring design constraints are met by software code development Builds engineering stack required for Data and AIML products, including data engineering, backend engineering, Cloud infra DevOps and MLOps Designs and implements data engineering solutions, leveraging modern big data technologies Drives adoption and governance of approved AI assisted engineering practices across teams to improve code quality, delivery speed, and operational outcomes (e.g., AI assisted code review/refactoring, test acceleration, release readiness, incident/root cause analysis), while establishing measurable validation standards (secure coding, peer review, automated testing) and promoting reuse of proven patterns and automation within the SDLC/TLM toolchain. Applies knowledge of tools within the Software Development Life Cycle toolchain, including approved AI assisted development and automation capabilities, to improve the value realized by automation at scale. Contributes to software engineering communities of practice and events that explore new and emerging technologies Embraces a passion for learning, problem solving, creative thinking and a can do attitude. Required qualifications, capabilities, and skills Formal training or certification onsoftware engineeringconcepts and proficient applied experience Hands on practical experience in system design, application development, testing, and operational stability Proficient in coding in one or more languages- Python Experience in developing, debugging, and maintaining code in a large corporate environment with one or more modern programming languages and database querying languages. Overall knowledge of the Software Development Life Cycle Proven track record in system design, architecting and developing microservices, distributed systems and data intensive applications Experience with Cloud services, Infrastructure as Code, containerized application development, big data and modern data engineering technologies Practical experience developing Production scale Cloud native data engineering solutions in commercial environments Familiarity with Cloud Data engineering services (e.g., ETL, Glue, S3, Athena) and MLOps stack Demonstrated experience leading effective use of enterprise authorized AI assisted software development tools within the work environment (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching senior engineers/leads on compliant usage patterns and controls. Ability to convey design choices and results clearly and communicate effectively to stakeholders of various backgrounds Preferred qualifications, capabilities, and skills Experience with data, AWS and AIML engineering in commercial settings, preferably in financial sector Experience working on recommendation systems, LLM applications or other AI/ML systems Practical experience with Kubernetes, EKS, Docker, MLOps Prior exposure to LLMs, RAG, Knowledge Graph Technologies, OpenSearch and vector databases Prior experience collaborating with data scientists
13/06/2026
Full time
We have an exciting and rewarding opportunity for you to take your software engineering career to the next level. As a Senior Lead Software Engineer at JPMorgan Chase within the Commercial & Investment Bank's Markets Research Technology team, you serve as a seasoned member of an agile team to design and deliver trusted market leading technology products in a secure, stable, and scalable way. You are responsible for carrying out critical technology solutions across multiple technical areas within various business functions in support of the firm's business objectives. You will work on challenging Cloud native data, backend engineering and AIML engineering, helping us industrialize AI/ML models at Production scale. This role is a technical hands on Engineering role. Experience with data science/ML modeling is advantageous but not essential to this role. Job responsibilities Executes software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems Creates secure and high quality production code and maintains algorithms that run synchronously with appropriate systems Produces architecture and design artifacts for complex applications while being accountable for ensuring design constraints are met by software code development Builds engineering stack required for Data and AIML products, including data engineering, backend engineering, Cloud infra DevOps and MLOps Designs and implements data engineering solutions, leveraging modern big data technologies Drives adoption and governance of approved AI assisted engineering practices across teams to improve code quality, delivery speed, and operational outcomes (e.g., AI assisted code review/refactoring, test acceleration, release readiness, incident/root cause analysis), while establishing measurable validation standards (secure coding, peer review, automated testing) and promoting reuse of proven patterns and automation within the SDLC/TLM toolchain. Applies knowledge of tools within the Software Development Life Cycle toolchain, including approved AI assisted development and automation capabilities, to improve the value realized by automation at scale. Contributes to software engineering communities of practice and events that explore new and emerging technologies Embraces a passion for learning, problem solving, creative thinking and a can do attitude. Required qualifications, capabilities, and skills Formal training or certification onsoftware engineeringconcepts and proficient applied experience Hands on practical experience in system design, application development, testing, and operational stability Proficient in coding in one or more languages- Python Experience in developing, debugging, and maintaining code in a large corporate environment with one or more modern programming languages and database querying languages. Overall knowledge of the Software Development Life Cycle Proven track record in system design, architecting and developing microservices, distributed systems and data intensive applications Experience with Cloud services, Infrastructure as Code, containerized application development, big data and modern data engineering technologies Practical experience developing Production scale Cloud native data engineering solutions in commercial environments Familiarity with Cloud Data engineering services (e.g., ETL, Glue, S3, Athena) and MLOps stack Demonstrated experience leading effective use of enterprise authorized AI assisted software development tools within the work environment (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching senior engineers/leads on compliant usage patterns and controls. Ability to convey design choices and results clearly and communicate effectively to stakeholders of various backgrounds Preferred qualifications, capabilities, and skills Experience with data, AWS and AIML engineering in commercial settings, preferably in financial sector Experience working on recommendation systems, LLM applications or other AI/ML systems Practical experience with Kubernetes, EKS, Docker, MLOps Prior exposure to LLMs, RAG, Knowledge Graph Technologies, OpenSearch and vector databases Prior experience collaborating with data scientists
We're looking for a hands-on AI engineer ready to take their career to new heights. Join the ranks of top talent at one of the world's most influential companies. As an Applied AI ML Lead - Vice President at JPMorgan Chase within the International Private Bank (IPB) Technology Artificial Intelligence and Machine Learning (AIML) Team, you provide deep engineering expertise and work across agile teams to enhance, build, and deliver trusted market leading AI products in a secure, stable, and scalable way. You will translate business problems into agentic AI and machine learning solutions, taking models from concept through to production grade services with measurable client and advisor impact. You will be responsible to the Head of AIML in IPB Tech for the end to end design, build, and production delivery of priority IPB AI/ML use cases, with particular focus on agentic AI applications, generative AI guardrails, and production ML supporting advisor and client journeys. Job responsibilities Owns end-to-end delivery of priority IPB AI/ML use cases, from problem framing and business case through to deployed, monitored production services with measurable advisor and client impact Leads the engineering build of agentic AI and LLM powered products serving IPB advisors and clients across the globe. Sets the engineering quality bar for the team's AI products through code reviews, technical design, and pairing with peers and junior engineers Establishes and operates Responsible AI controls in production (guardrails, evaluation frameworks, observability, and model risk controls) to firm wide standards Acts as a primary technical partner to IPB business stakeholders, surfacing new AI/ML opportunities and shaping them into funded workstreams Represents the AIML team in firm wide AI/ML governance and engineering forums; ensures cross-border, regulatory, and data privacy considerations are reflected in solution design Contributes to the team's GenAI education programme through training content, knowledge sharing sessions, and mentoring of junior engineers and interns Champions the firm's culture of diversity, Opportunity, inclusion, and respect Required qualifications, capabilities, and skills Formal training or certification in software engineering concepts and expert applied experience Advanced proficiency in Python and modern software engineering practices (testing, design patterns, code review, version control) Fluent with AI coding tools (e.g., Claude Code, GitHub Copilot) as a core part of day to day software development, with the judgement to know when to lean on them and when not to Hands on experience building, evaluating, and deploying machine learning models into production Practical experience with Large Language Models, including prompt engineering, RAG, fine tuning, agentic frameworks, skills. Demonstrated experience delivering system design, application development, testing, and operational stability for ML or data intensive systems Strong communication skills with confidence engaging senior business stakeholders and translating technical concepts for non technical audiences Experience applying new methods to determine solutions for complex technology problems across multiple technical disciplines MSc in Computer Science, Data Science, Engineering, or a related quantitative field Preferred qualifications, capabilities, and skills Postgraduate level qualification in data science, artificial intelligence, or machine learning Practical experience with CI/CD, containerization, and cloud native deployment patterns Experience within financial services technology, particularly wealth, private banking, or asset management Experience with Databricks, Kubernetes, or comparable ML / cloud platforms Experience designing or contributing to AI governance, model validation, or guardrail frameworks
09/06/2026
Full time
We're looking for a hands-on AI engineer ready to take their career to new heights. Join the ranks of top talent at one of the world's most influential companies. As an Applied AI ML Lead - Vice President at JPMorgan Chase within the International Private Bank (IPB) Technology Artificial Intelligence and Machine Learning (AIML) Team, you provide deep engineering expertise and work across agile teams to enhance, build, and deliver trusted market leading AI products in a secure, stable, and scalable way. You will translate business problems into agentic AI and machine learning solutions, taking models from concept through to production grade services with measurable client and advisor impact. You will be responsible to the Head of AIML in IPB Tech for the end to end design, build, and production delivery of priority IPB AI/ML use cases, with particular focus on agentic AI applications, generative AI guardrails, and production ML supporting advisor and client journeys. Job responsibilities Owns end-to-end delivery of priority IPB AI/ML use cases, from problem framing and business case through to deployed, monitored production services with measurable advisor and client impact Leads the engineering build of agentic AI and LLM powered products serving IPB advisors and clients across the globe. Sets the engineering quality bar for the team's AI products through code reviews, technical design, and pairing with peers and junior engineers Establishes and operates Responsible AI controls in production (guardrails, evaluation frameworks, observability, and model risk controls) to firm wide standards Acts as a primary technical partner to IPB business stakeholders, surfacing new AI/ML opportunities and shaping them into funded workstreams Represents the AIML team in firm wide AI/ML governance and engineering forums; ensures cross-border, regulatory, and data privacy considerations are reflected in solution design Contributes to the team's GenAI education programme through training content, knowledge sharing sessions, and mentoring of junior engineers and interns Champions the firm's culture of diversity, Opportunity, inclusion, and respect Required qualifications, capabilities, and skills Formal training or certification in software engineering concepts and expert applied experience Advanced proficiency in Python and modern software engineering practices (testing, design patterns, code review, version control) Fluent with AI coding tools (e.g., Claude Code, GitHub Copilot) as a core part of day to day software development, with the judgement to know when to lean on them and when not to Hands on experience building, evaluating, and deploying machine learning models into production Practical experience with Large Language Models, including prompt engineering, RAG, fine tuning, agentic frameworks, skills. Demonstrated experience delivering system design, application development, testing, and operational stability for ML or data intensive systems Strong communication skills with confidence engaging senior business stakeholders and translating technical concepts for non technical audiences Experience applying new methods to determine solutions for complex technology problems across multiple technical disciplines MSc in Computer Science, Data Science, Engineering, or a related quantitative field Preferred qualifications, capabilities, and skills Postgraduate level qualification in data science, artificial intelligence, or machine learning Practical experience with CI/CD, containerization, and cloud native deployment patterns Experience within financial services technology, particularly wealth, private banking, or asset management Experience with Databricks, Kubernetes, or comparable ML / cloud platforms Experience designing or contributing to AI governance, model validation, or guardrail frameworks