C005309 AI Engineer (NS) - THU 24 Sep

• Identify issues in machine learning systems, models, pipelines, datasets, and development activities, and implement practical improvements.
• Design, develop, test, document, amend, refactor, and maintain moderately complex programs, scripts, and AI/ML components.
• Apply agreed engineering standards, tools, and secure development practices to deliver reliable, maintainable, and well-engineered solutions.
• Support AI/software lifecycle engineering by eliciting requirements, selecting suitable working practices, and deploying automation for development, testing, release, deployment, and monitoring.
• Define AI modules for integration builds, produce build definitions, and validate completed modules against agreed functional, quality, security, and performance criteria.
• Build, maintain, and improve data pipelines using data engineering standards and tools, including ETL/ELT processes.
• Monitor progress, report status, communicate risks or blockers, and collaborate with colleagues through reviews and shared delivery ownership.
• Support monitoring of emerging technologies, contribute to technology assessments, reports, roadmaps, and knowledge sharing.

Skills, Knowledge & Experience:

• The candidate must have a currently active NATO SECRET security clearance
• Experience developing, optimising, deploying, and maintaining end-to-end AI/ML pipelines, including training, packaging, monitoring, and lifecycle management.
• Strong hands-on experience in programming, machine learning, software engineering, and applied AI development.
• Solid understanding of machine learning concepts, model evaluation, performance measurement, assessment methods, and model improvement techniques.
• Experience applying pre-trained models, foundation models, LLMs, and Generative AI to practical use cases.
• Experience with RAG, embeddings, vector databases, AI application architectures, and production-grade AI agent backends using frameworks such as LangChain, LlamaIndex, Pydantic AI, or similar.
• Strong experience with MLOps/AIOps, version control, CI/CD, automation, experiment/model lifecycle practices, and build/release workflows.
• Experience developing REST APIs, backend services, and modern Python applications using FastAPI, Pydantic, or similar frameworks.
• Experience with containerisation, orchestration, and deployment technologies including Docker, Kubernetes, Helm, cloud infrastructure provisioning, and workflow orchestration tools such as Airflow or Argo.
• Experience implementing guardrails, observability, logging, monitoring, and operational controls for LLM-based systems.
• Experience working with SQL and NoSQL databases.
• Experience with TypeScript, Node.js, or frontend frameworks such as Next.js.
• Experience working in secure, restricted, or air-gapped environments.