Manager of Artificial Intelligence

Fimador
City of London
1 month ago
Applications closed

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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.

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