Manager. Regulatory Policy in England

Our client is expanding its advanced AI capability across mission-critical platforms used in environments worldwide. Fimador are seeking a rare blend of hands-on AI engineer, technical authority, and team leader — to shape how their intelligent systems are designed, built, and responsibly deployed at scale.

This is not a management-only role. This is for someone who still loves to build, solve complex engineering problems, guide other engineers, and set the technical direction for AI across large, high-impact systems.

Key Responsibilities:

• Architect, design, and deliver scalable AI and machine learning solutions end-to-end.
• Remain hands-on in building complex AI components and solving model or system challenges.
• Lead, mentor, and technically guide a team of AI engineers and developers.
• Define engineering standards, development practices, and quality benchmarks for AI initiatives.
• Own technical estimations, feasibility assessments, and delivery quality across AI workstreams.
• Lead technical troubleshooting, root cause analysis, and performance optimisation.
• Collaborate closely with architecture, product, and DevOps teams to embed AI into larger ecosystems.
• Continuously assess and introduce new AI tools, frameworks, and approaches to maintain innovation.

About you:

This role suits a senior engineer who has evolved into a technical leader without losing their passion for building.

• Ideally a degree in Computer Science, Engineering, or similar.
• 5+ years of software engineering experience using modern object-oriented languages (e.g., Java, C#, Python).
• Strong Python capability alongside at least one other major language.
• Practical experience building with Generative AI technologies in real solutions.
• Experience leading or mentoring engineering teams (6+ engineers).
• Strong foundations in OOP, system design, and engineering best practices.
• Hands-on knowledge of AI/ML frameworks such as TensorFlow, PyTorch, or scikit-learn.
• Experience with orchestration/integration frameworks such as LangChain or Semantic Kernel.
• Solid understanding of ML concepts (supervised/unsupervised learning, transformers, CNNs/RNNs, model evaluation).
• Experience with prompt engineering, RAG pipelines, and model fine-tuning.
• The judgement to identify where AI adds value — and where it doesn’t.
• Experience deploying and operationalising LLMs (exposure to Microsoft AI Foundry or Ollama advantageous).
• Strong grasp of CI/CD, testing, version control, secure coding, and modern engineering workflows.
• Exposure to MLOps tools such as MLflow, Kubeflow, or Azure ML.