Rokstone Group
Role Summary As an AI Engineer, you will design, implement, and productionise behavioural learning systems that integrate directly into our digital products and workflows. Your focus will be on turning advanced behavioural, sequential, and causal AI models into reliable, scalable, and maintainable production systems that power Digital Twins, agentic decision engines, and intelligent automation across our digital suite. This role bridges model development and real world implementation. You will work hands on with software engineers, ML engineers, and product teams to ensure behavioural intelligence is embedded end to end from data pipelines and inference services through to live product decisioning and monitoring. Key Responsibilities Design, implement, and deploy AI models that predict, optimise, or automate decision making within production digital workflows. Translate behavioural and sequential modelling approaches (e.g. sequence prediction, intent modelling, imitation learning) into robust, production ready systems. Build and maintain end to end AI pipelines, including data ingestion, feature engineering, model training, inference, and monitoring. Apply causal inference techniques to evaluate the real world impact of AI driven decisions and support data informed product changes. Integrate AI services into existing platforms via APIs, microservices, and event driven architectures in close collaboration with engineering teams. Partner with product and platform teams to ensure AI outputs are actionable, explainable, and aligned with business workflows. Support Digital Twin and agentic systems by implementing behavioural dynamics, state modelling, and decision process representations. Validate deployed models using offline replay, A/B testing, shadow deployments, and simulation frameworks. Ensure solutions meet production standards for scalability, reliability, security, and observability. Contribute to engineering best practices around testing, versioning, CI/CD, and model lifecycle management. Role Requirements Strong foundation in machine learning, applied statistics, or software engineering with a focus on building production systems. Demonstrated experience implementing sequence or decision based models (e.g. LSTM, Transformers, Markov models, RL inspired methods) in real applications. Practical experience deploying AI models into production environments, not just experimentation or notebooks. Familiarity with behavioural modelling concepts such as imitation learning, behavioural cloning, or decision process modelling. Working experience with causal inference tooling or methodologies (e.g. DoWhy, EconML, CausalML) applied to real data. Strong Python skills and experience with ML frameworks such as PyTorch or TensorFlow. Experience building or integrating AI systems using APIs, services, pipelines, or orchestration frameworks. Comfortable collaborating in engineering led product environments, balancing research insight with delivery constraints. Strong problem solving skills, with the ability to iterate quickly from prototype to production. Management Duties No We are an equal opportunity employer, and we are proud to share that 93% of our employees say they can be themselves at work. We aim to hire our industry's finest people because the best people drive the best outcomes. And we forever challenge the status quo because we know there are always ways to improve things. Because together, we're limitless. We value applicants from all backgrounds and foster a culture of inclusivity. We understand the need for flexibility, so work in a hybrid model. Please let us know if you require any reasonable adjustments during the recruitment process.
Role Summary As an AI Engineer, you will design, implement, and productionise behavioural learning systems that integrate directly into our digital products and workflows. Your focus will be on turning advanced behavioural, sequential, and causal AI models into reliable, scalable, and maintainable production systems that power Digital Twins, agentic decision engines, and intelligent automation across our digital suite. This role bridges model development and real world implementation. You will work hands on with software engineers, ML engineers, and product teams to ensure behavioural intelligence is embedded end to end from data pipelines and inference services through to live product decisioning and monitoring. Key Responsibilities Design, implement, and deploy AI models that predict, optimise, or automate decision making within production digital workflows. Translate behavioural and sequential modelling approaches (e.g. sequence prediction, intent modelling, imitation learning) into robust, production ready systems. Build and maintain end to end AI pipelines, including data ingestion, feature engineering, model training, inference, and monitoring. Apply causal inference techniques to evaluate the real world impact of AI driven decisions and support data informed product changes. Integrate AI services into existing platforms via APIs, microservices, and event driven architectures in close collaboration with engineering teams. Partner with product and platform teams to ensure AI outputs are actionable, explainable, and aligned with business workflows. Support Digital Twin and agentic systems by implementing behavioural dynamics, state modelling, and decision process representations. Validate deployed models using offline replay, A/B testing, shadow deployments, and simulation frameworks. Ensure solutions meet production standards for scalability, reliability, security, and observability. Contribute to engineering best practices around testing, versioning, CI/CD, and model lifecycle management. Role Requirements Strong foundation in machine learning, applied statistics, or software engineering with a focus on building production systems. Demonstrated experience implementing sequence or decision based models (e.g. LSTM, Transformers, Markov models, RL inspired methods) in real applications. Practical experience deploying AI models into production environments, not just experimentation or notebooks. Familiarity with behavioural modelling concepts such as imitation learning, behavioural cloning, or decision process modelling. Working experience with causal inference tooling or methodologies (e.g. DoWhy, EconML, CausalML) applied to real data. Strong Python skills and experience with ML frameworks such as PyTorch or TensorFlow. Experience building or integrating AI systems using APIs, services, pipelines, or orchestration frameworks. Comfortable collaborating in engineering led product environments, balancing research insight with delivery constraints. Strong problem solving skills, with the ability to iterate quickly from prototype to production. Management Duties No We are an equal opportunity employer, and we are proud to share that 93% of our employees say they can be themselves at work. We aim to hire our industry's finest people because the best people drive the best outcomes. And we forever challenge the status quo because we know there are always ways to improve things. Because together, we're limitless. We value applicants from all backgrounds and foster a culture of inclusivity. We understand the need for flexibility, so work in a hybrid model. Please let us know if you require any reasonable adjustments during the recruitment process.
Rokstone Group
Role Summary We are seeking an exceptional Principal Software Engineer - AI Native to lead the design, development, and delivery of business critical software platforms across Aventum Group. This is a hands on leadership role for a highly experienced software engineer who combines deep technical expertise with an AI first mindset. You will be responsible for architecting scalable solutions, driving engineering excellence, and leveraging AI native development practices to accelerate software delivery and innovation across the business. As a Principal Engineer, you will act as a technical authority across multiple initiatives, influencing architecture, engineering standards, AI adoption, cloud strategy, and software delivery practices whilst remaining actively involved in solution design and development. Key Accountabilities Technical Leadership & Architecture Define and drive engineering standards, architecture principles, and software development best practices. Design scalable, resilient, and secure enterprise solutions across frontend, backend, data, and cloud environments. Provide technical leadership across multiple engineering teams and strategic initiatives. Lead architectural decision making and technology selection processes. Champion modern engineering approaches including microservices, event driven architectures, APIs, and distributed systems. Drive technical governance while maintaining delivery speed and engineering quality. Software Engineering Build and deliver high quality software solutions using TypeScript, React, C#, and .NET and cloud native technologies. Design and develop APIs, integrations, and platform services. Take ownership of solution delivery from specification through deployment and ongoing optimisation. Review code and ensure engineering quality, maintainability, security, and performance. Support complex troubleshooting, performance tuning, and root cause analysis. AI Native Engineering Lead adoption of AI assisted development tools and agentic AI workflows. Apply Spec Driven Development principles to improve software quality and delivery speed. Use AI to accelerate requirements gathering, technical specifications, coding, testing, documentation, and deployment; validate and assure the quality of AI generated outputs. Drive productivity improvements through AI enabled engineering practices. Cloud & DevOps Design and deliver cloud native solutions primarily within Microsoft Azure. Improve CI/CD pipelines, deployment automation, and Infrastructure as Code capabilities. Implement secure, scalable, and observable cloud architectures. Drive best practice across monitoring, resilience, security, and operational excellence. Collaboration & Influence Partner with Product, Data, Technology, and Business stakeholders. Translate complex business requirements into scalable technical solutions. Mentor engineers and contribute to knowledge sharing across the engineering function. Influence the future direction of software engineering and AI adoption within Aventum. Any additional duties as assigned. Role Requirements Significant experience as a Senior, Lead, Staff, or Principal Software Engineer. Strong expertise in TypeScript, React, C#, and .NET. Proven experience building enterprise scale applications and distributed systems. Deep understanding of software architecture, design patterns, testing, and engineering best practices. Experience building APIs, integrations, and microservices. Strong understanding of secure software development principles. Hands on experience with Microsoft Azure, AWS, or GCP. Experience with Docker, Kubernetes, CI/CD pipelines, Infrastructure as Code (Terraform or equivalent). Strong understanding of cloud native architecture and operational excellence. Strong database design and optimisation experience. Experience with relational and NoSQL databases. Understanding of complex data flows, integration architectures, and performance optimisation. Practical experience using AI assisted engineering tools. Strong AI literacy and understanding of agentic AI workflows; experience reviewing and validating AI generated code and technical outputs. Demonstrated use of AI to improve engineering productivity and delivery outcomes. Skills and Abilities Technical Excellence Enterprise architecture and solution design Full stack engineering expertise Cloud native development Distributed systems and integration design DevOps, CI/CD, and Infrastructure as Code Database and data engineering principles Leadership & Influence Strong stakeholder management skills Ability to influence technical direction without direct authority Excellent communication and presentation skills Ability to simplify complex technical concepts for non technical audiences AI First Mindset Passion for emerging technologies and AI innovation Continuous improvement mentality; ability to combine engineering judgement with AI enabled productivity Focus on delivering business value through technology We are an equal opportunity employer, and we are proud to share that 93% of our employees say they can be themselves at work. We aim to hire our industry's finest people because the best people drive the best outcomes. And we forever challenge the status quo because we know there are always ways to improve things. Because together, we're limitless. We value applicants from all backgrounds and foster a culture of inclusivity. We understand the need for flexibility, so work in a hybrid model. Please let us know if you require any reasonable adjustments during the recruitment process.
Role Summary We are seeking an exceptional Principal Software Engineer - AI Native to lead the design, development, and delivery of business critical software platforms across Aventum Group. This is a hands on leadership role for a highly experienced software engineer who combines deep technical expertise with an AI first mindset. You will be responsible for architecting scalable solutions, driving engineering excellence, and leveraging AI native development practices to accelerate software delivery and innovation across the business. As a Principal Engineer, you will act as a technical authority across multiple initiatives, influencing architecture, engineering standards, AI adoption, cloud strategy, and software delivery practices whilst remaining actively involved in solution design and development. Key Accountabilities Technical Leadership & Architecture Define and drive engineering standards, architecture principles, and software development best practices. Design scalable, resilient, and secure enterprise solutions across frontend, backend, data, and cloud environments. Provide technical leadership across multiple engineering teams and strategic initiatives. Lead architectural decision making and technology selection processes. Champion modern engineering approaches including microservices, event driven architectures, APIs, and distributed systems. Drive technical governance while maintaining delivery speed and engineering quality. Software Engineering Build and deliver high quality software solutions using TypeScript, React, C#, and .NET and cloud native technologies. Design and develop APIs, integrations, and platform services. Take ownership of solution delivery from specification through deployment and ongoing optimisation. Review code and ensure engineering quality, maintainability, security, and performance. Support complex troubleshooting, performance tuning, and root cause analysis. AI Native Engineering Lead adoption of AI assisted development tools and agentic AI workflows. Apply Spec Driven Development principles to improve software quality and delivery speed. Use AI to accelerate requirements gathering, technical specifications, coding, testing, documentation, and deployment; validate and assure the quality of AI generated outputs. Drive productivity improvements through AI enabled engineering practices. Cloud & DevOps Design and deliver cloud native solutions primarily within Microsoft Azure. Improve CI/CD pipelines, deployment automation, and Infrastructure as Code capabilities. Implement secure, scalable, and observable cloud architectures. Drive best practice across monitoring, resilience, security, and operational excellence. Collaboration & Influence Partner with Product, Data, Technology, and Business stakeholders. Translate complex business requirements into scalable technical solutions. Mentor engineers and contribute to knowledge sharing across the engineering function. Influence the future direction of software engineering and AI adoption within Aventum. Any additional duties as assigned. Role Requirements Significant experience as a Senior, Lead, Staff, or Principal Software Engineer. Strong expertise in TypeScript, React, C#, and .NET. Proven experience building enterprise scale applications and distributed systems. Deep understanding of software architecture, design patterns, testing, and engineering best practices. Experience building APIs, integrations, and microservices. Strong understanding of secure software development principles. Hands on experience with Microsoft Azure, AWS, or GCP. Experience with Docker, Kubernetes, CI/CD pipelines, Infrastructure as Code (Terraform or equivalent). Strong understanding of cloud native architecture and operational excellence. Strong database design and optimisation experience. Experience with relational and NoSQL databases. Understanding of complex data flows, integration architectures, and performance optimisation. Practical experience using AI assisted engineering tools. Strong AI literacy and understanding of agentic AI workflows; experience reviewing and validating AI generated code and technical outputs. Demonstrated use of AI to improve engineering productivity and delivery outcomes. Skills and Abilities Technical Excellence Enterprise architecture and solution design Full stack engineering expertise Cloud native development Distributed systems and integration design DevOps, CI/CD, and Infrastructure as Code Database and data engineering principles Leadership & Influence Strong stakeholder management skills Ability to influence technical direction without direct authority Excellent communication and presentation skills Ability to simplify complex technical concepts for non technical audiences AI First Mindset Passion for emerging technologies and AI innovation Continuous improvement mentality; ability to combine engineering judgement with AI enabled productivity Focus on delivering business value through technology We are an equal opportunity employer, and we are proud to share that 93% of our employees say they can be themselves at work. We aim to hire our industry's finest people because the best people drive the best outcomes. And we forever challenge the status quo because we know there are always ways to improve things. Because together, we're limitless. We value applicants from all backgrounds and foster a culture of inclusivity. We understand the need for flexibility, so work in a hybrid model. Please let us know if you require any reasonable adjustments during the recruitment process.