Koud
Koud is looking for a Technical Delivery Lead to work with one of our clients. We are seeking a Technical Delivery Lead to drive the internal delivery of AI and digital transformation projects. You will oversee engineering execution, QA, risks, and dependencies, ensuring delivery of production-ready solutions while keeping the team aligned and unblocked. This is not a client-facing role. You will focus on internal delivery excellence, including architecture oversight, engineering standards, and team mentorship. Fluent English and international project experience are required. Key Responsibilities: Provide hands on technical leadership across AI/ML, automation, and integration; Review and approve system architecture, technical designs, and solution proposals; Ensure solutions meet standards of performance, scalability, security, and maintainability; Enforce coding standards, design patterns, and technical best practices across the team; Own and drive the QA strategy across all delivery workstreams, including automated testing, code review gates, and acceptance criteria; Define and track engineering KPIs (code coverage, defect escape rate, deployment frequency, MTTR, and change failure rate); Promote shift left testing practices, including AI model validation, integration testing, and performance benchmarking; Lead post incident reviews (PIRs) and blameless retrospectives to drive continuous improvement; Ensure compliance with industry standards and regulations (SOC 2, ISO 27001, GDPR); Drive technical delivery across multiple projects using Agile/Scrum methodologies; Partner with Scrum Master, engineering leads, and engagement owner to define sprint goals, manage dependencies, and remove blockers; Own technical risks, mitigation plans, and escalation, surfacing issues early; Coordinate cross functional teams (AI/ML, data, DevOps, automation, QA); Prepare technical reports, roadmaps, and delivery milestones for internal review; Translate high level requirements into actionable technical work for the engineering team; Mentor and coach senior engineers, fostering a culture of technical excellence and continuous learning; Lead hiring efforts, including technical interview design, candidate evaluation, and onboarding; Drive adoption of emerging technologies, frameworks, and AI/ML best practices across the team; Promote knowledge sharing through tech talks, brown bags, ADRs, and internal documentation. Requirements: Progressive experience in software engineering, with solid experience in a technical delivery lead, engineering manager, or technical program manager role; Proven delivery experience in international or multi region projects; Proven experience delivering complex, multi workstream AI/ML or enterprise software projects end to end; Deep knowledge of modern AI/ML stack: LLMs, RAG, vector databases, and agentic frameworks; Hands on experience with at least two languages (Python, TypeScript/JavaScript, Go, or Java); Experience with cloud platforms (AWS, Azure, GCP) and infrastructure as code (Terraform, Pulumi); Strong QA expertise (automated testing, CI/CD, observability); Ability to clearly communicate technical status, risks, and trade offs; Strong understanding of Agile/Scrum (planning, backlog, velocity); Experience with compliance frameworks (SOC 2, ISO 27001, GDPR, HIPAA, or similar); Fluent English and experience working on international, multicultural teams; Strong communication, stakeholder management, and problem solving skills. Preferred Qualifications: Bachelor's degree in Computer Science, Information Systems, Engineering, or a related field; Previous experience mentoring engineers or acting as a technical lead; Experience in insurance, financial services, or other highly regulated industries; Familiarity with MLOps practices: model versioning, A/B testing, drift detection, and automated retraining pipelines; Certifications: PMP, SAFe, AWS Solutions Architect, Azure Solutions Architect, or equivalent; Experience managing hybrid onshore/offshore delivery teams; Track record of building and scaling AI Centers of Excellence or innovation teams; Familiarity with AI governance frameworks, responsible AI practices, and EU AI Act compliance; Experience with platform engineering concepts: developer portals, internal developer platforms (IDPs), and self service infrastructure. Working Model & Collaboration: Brazil based role with a 100% remote working model; Close collaboration with international stakeholders and teams across regions; Schedule flexibility may occasionally be required for critical milestones or major incidents.
Koud is looking for a Technical Delivery Lead to work with one of our clients. We are seeking a Technical Delivery Lead to drive the internal delivery of AI and digital transformation projects. You will oversee engineering execution, QA, risks, and dependencies, ensuring delivery of production-ready solutions while keeping the team aligned and unblocked. This is not a client-facing role. You will focus on internal delivery excellence, including architecture oversight, engineering standards, and team mentorship. Fluent English and international project experience are required. Key Responsibilities: Provide hands on technical leadership across AI/ML, automation, and integration; Review and approve system architecture, technical designs, and solution proposals; Ensure solutions meet standards of performance, scalability, security, and maintainability; Enforce coding standards, design patterns, and technical best practices across the team; Own and drive the QA strategy across all delivery workstreams, including automated testing, code review gates, and acceptance criteria; Define and track engineering KPIs (code coverage, defect escape rate, deployment frequency, MTTR, and change failure rate); Promote shift left testing practices, including AI model validation, integration testing, and performance benchmarking; Lead post incident reviews (PIRs) and blameless retrospectives to drive continuous improvement; Ensure compliance with industry standards and regulations (SOC 2, ISO 27001, GDPR); Drive technical delivery across multiple projects using Agile/Scrum methodologies; Partner with Scrum Master, engineering leads, and engagement owner to define sprint goals, manage dependencies, and remove blockers; Own technical risks, mitigation plans, and escalation, surfacing issues early; Coordinate cross functional teams (AI/ML, data, DevOps, automation, QA); Prepare technical reports, roadmaps, and delivery milestones for internal review; Translate high level requirements into actionable technical work for the engineering team; Mentor and coach senior engineers, fostering a culture of technical excellence and continuous learning; Lead hiring efforts, including technical interview design, candidate evaluation, and onboarding; Drive adoption of emerging technologies, frameworks, and AI/ML best practices across the team; Promote knowledge sharing through tech talks, brown bags, ADRs, and internal documentation. Requirements: Progressive experience in software engineering, with solid experience in a technical delivery lead, engineering manager, or technical program manager role; Proven delivery experience in international or multi region projects; Proven experience delivering complex, multi workstream AI/ML or enterprise software projects end to end; Deep knowledge of modern AI/ML stack: LLMs, RAG, vector databases, and agentic frameworks; Hands on experience with at least two languages (Python, TypeScript/JavaScript, Go, or Java); Experience with cloud platforms (AWS, Azure, GCP) and infrastructure as code (Terraform, Pulumi); Strong QA expertise (automated testing, CI/CD, observability); Ability to clearly communicate technical status, risks, and trade offs; Strong understanding of Agile/Scrum (planning, backlog, velocity); Experience with compliance frameworks (SOC 2, ISO 27001, GDPR, HIPAA, or similar); Fluent English and experience working on international, multicultural teams; Strong communication, stakeholder management, and problem solving skills. Preferred Qualifications: Bachelor's degree in Computer Science, Information Systems, Engineering, or a related field; Previous experience mentoring engineers or acting as a technical lead; Experience in insurance, financial services, or other highly regulated industries; Familiarity with MLOps practices: model versioning, A/B testing, drift detection, and automated retraining pipelines; Certifications: PMP, SAFe, AWS Solutions Architect, Azure Solutions Architect, or equivalent; Experience managing hybrid onshore/offshore delivery teams; Track record of building and scaling AI Centers of Excellence or innovation teams; Familiarity with AI governance frameworks, responsible AI practices, and EU AI Act compliance; Experience with platform engineering concepts: developer portals, internal developer platforms (IDPs), and self service infrastructure. Working Model & Collaboration: Brazil based role with a 100% remote working model; Close collaboration with international stakeholders and teams across regions; Schedule flexibility may occasionally be required for critical milestones or major incidents.
Koud
Koud is looking for a Senior AI Security Engineer to work with one of our clients. We are seeking a Senior AI Security Engineer to lead the security of AI-powered products, platforms, and infrastructure. You will operate at the intersection of cybersecurity and AI, addressing emerging threats while enabling secure and scalable AI delivery. This role covers the full AI security lifecycle, including threat modeling, prompt injection defenses, securing model supply chains, hardening RAG pipelines, and building scalable security tooling. You will act as the subject matter expert on AI security, responsible AI, and compliance (e.g., EU AI Act). Fluent English and international project experience are required. Key Responsibilities Design and implement security for LLM apps, agents, and copilots; Build defenses against AI threats (prompt injection, jailbreaking, data poisoning, etc.); Secure RAG pipelines (data isolation, access control, context integrity); Implement content safety (filtering, toxicity detection); Enforce authentication, authorization, and rate limiting for AI APIs; Secure model serving (logging, audit trails, anomaly detection; Conduct threat modeling (STRIDE, MITRE ATLAS, OWASP LLM Top 10); Lead red teaming (adversarial prompts, robustness testing, data exfiltration); Track AI threat intelligence (attacks, CVEs, research); Build automated adversarial testing; Assess security of third party AI tools and models; Ensure compliance (EU AI Act, NIST AI RMF, ISO 42001); Define AI security policies (access, data, prompts, monitoring); Partner with legal/compliance on governance, consent, and bias; Maintain model documentation, risk assessments, and standards; Enforce responsible AI (fairness, transparency, oversight); Build AI security tools (prompt injection scanners, vulnerability scanning); Implement monitoring and alerting (SIEM/SOAR); Develop reusable security guardrails and middleware; Apply security as code (policy as code, infra scanning, secrets); Enable real time detection and forensic analysis; Embed with engineering teams to ensure secure by design AI; Provide security guidance across product and engineering; Lead AI security training and awareness; Support incident response (model compromise, data leaks, attacks); Act as internal AI security expert and documentation owner. Requirements Extensive experience in cybersecurity, application security, or security engineering, with focus on AI/ML security; Deep understanding of LLM security risks (prompt injection, jailbreaking, data leakage, OWASP LLM Top 10); Hands on experience securing AI/ML systems in production (model serving, RAG, agents, APIs); Strong software engineering skills (Python + one of Go, TypeScript, Rust, or Java); Experience with cloud security (AWS, Azure, or GCP - IAM, network, encryption, secrets); Proficiency with security tools (SAST, DAST, SCA, SIEM, vulnerability management); Expertise in authentication/authorization (OAuth2, OIDC, SAML, RBAC/ABAC, zero trust); Strong knowledge of Secure SDLC and DevSecOps practices; Ability to communicate AI security risks to technical and non technical stakeholders; Fluent English and experience with international, multicultural teams; Strong communication, stakeholder management, and problem solving skills. Preferred Qualifications Bachelor's degree in Computer Science, Information Systems, Engineering, or a related field; Previous experience mentoring engineers or acting as a technical lead; Experience in insurance, financial services, or healthcare - industries with high regulatory and data privacy requirements; Hands on experience with AI/ML frameworks: LangChain, LangGraph, Hugging Face Transformers, vLLM, Ollama, and AI agent frameworks (CrewAI, AutoGen); Familiarity with AI security tools: Garak, Rebuff, NeMo Guardrails (NVIDIA), Prompt Guard, LLM Guard, Lakera Guard; Experience with vector database security: Pinecone, Weaviate, ChromaDB, pgvector access control and data isolation; Knowledge of emerging AI standards: MCP (Model Context Protocol), Agent to Agent (A2A) Protocol, and AI gateway patterns; Security certifications: CISSP, CISM, OSCP, GIAC (GPEN/GWAPT), or cloud specific security certs (AWS Security Specialty, AZ 500); Experience with AI governance platforms and model risk management frameworks; Published research, blog posts, or conference talks on AI security topics; Experience building AI powered security tools (using AI to enhance security operations, not just securing AI) Working Model & Collaboration Brazil based role with a 100% remote working model; Close collaboration with international stakeholders and teams across regions; Schedule flexibility may occasionally be required for critical milestones or major incidents.
Koud is looking for a Senior AI Security Engineer to work with one of our clients. We are seeking a Senior AI Security Engineer to lead the security of AI-powered products, platforms, and infrastructure. You will operate at the intersection of cybersecurity and AI, addressing emerging threats while enabling secure and scalable AI delivery. This role covers the full AI security lifecycle, including threat modeling, prompt injection defenses, securing model supply chains, hardening RAG pipelines, and building scalable security tooling. You will act as the subject matter expert on AI security, responsible AI, and compliance (e.g., EU AI Act). Fluent English and international project experience are required. Key Responsibilities Design and implement security for LLM apps, agents, and copilots; Build defenses against AI threats (prompt injection, jailbreaking, data poisoning, etc.); Secure RAG pipelines (data isolation, access control, context integrity); Implement content safety (filtering, toxicity detection); Enforce authentication, authorization, and rate limiting for AI APIs; Secure model serving (logging, audit trails, anomaly detection; Conduct threat modeling (STRIDE, MITRE ATLAS, OWASP LLM Top 10); Lead red teaming (adversarial prompts, robustness testing, data exfiltration); Track AI threat intelligence (attacks, CVEs, research); Build automated adversarial testing; Assess security of third party AI tools and models; Ensure compliance (EU AI Act, NIST AI RMF, ISO 42001); Define AI security policies (access, data, prompts, monitoring); Partner with legal/compliance on governance, consent, and bias; Maintain model documentation, risk assessments, and standards; Enforce responsible AI (fairness, transparency, oversight); Build AI security tools (prompt injection scanners, vulnerability scanning); Implement monitoring and alerting (SIEM/SOAR); Develop reusable security guardrails and middleware; Apply security as code (policy as code, infra scanning, secrets); Enable real time detection and forensic analysis; Embed with engineering teams to ensure secure by design AI; Provide security guidance across product and engineering; Lead AI security training and awareness; Support incident response (model compromise, data leaks, attacks); Act as internal AI security expert and documentation owner. Requirements Extensive experience in cybersecurity, application security, or security engineering, with focus on AI/ML security; Deep understanding of LLM security risks (prompt injection, jailbreaking, data leakage, OWASP LLM Top 10); Hands on experience securing AI/ML systems in production (model serving, RAG, agents, APIs); Strong software engineering skills (Python + one of Go, TypeScript, Rust, or Java); Experience with cloud security (AWS, Azure, or GCP - IAM, network, encryption, secrets); Proficiency with security tools (SAST, DAST, SCA, SIEM, vulnerability management); Expertise in authentication/authorization (OAuth2, OIDC, SAML, RBAC/ABAC, zero trust); Strong knowledge of Secure SDLC and DevSecOps practices; Ability to communicate AI security risks to technical and non technical stakeholders; Fluent English and experience with international, multicultural teams; Strong communication, stakeholder management, and problem solving skills. Preferred Qualifications Bachelor's degree in Computer Science, Information Systems, Engineering, or a related field; Previous experience mentoring engineers or acting as a technical lead; Experience in insurance, financial services, or healthcare - industries with high regulatory and data privacy requirements; Hands on experience with AI/ML frameworks: LangChain, LangGraph, Hugging Face Transformers, vLLM, Ollama, and AI agent frameworks (CrewAI, AutoGen); Familiarity with AI security tools: Garak, Rebuff, NeMo Guardrails (NVIDIA), Prompt Guard, LLM Guard, Lakera Guard; Experience with vector database security: Pinecone, Weaviate, ChromaDB, pgvector access control and data isolation; Knowledge of emerging AI standards: MCP (Model Context Protocol), Agent to Agent (A2A) Protocol, and AI gateway patterns; Security certifications: CISSP, CISM, OSCP, GIAC (GPEN/GWAPT), or cloud specific security certs (AWS Security Specialty, AZ 500); Experience with AI governance platforms and model risk management frameworks; Published research, blog posts, or conference talks on AI security topics; Experience building AI powered security tools (using AI to enhance security operations, not just securing AI) Working Model & Collaboration Brazil based role with a 100% remote working model; Close collaboration with international stakeholders and teams across regions; Schedule flexibility may occasionally be required for critical milestones or major incidents.