AI Engineer

Essential Qualifications/Experience:

·       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

DUTIES/ROLE:

·       Apply machine learning and data science techniques to new problems and datasets, including evaluating model outcomes, performance, and data quality

·       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