Lead AI Engineer

Your Objectives

• Solve the hardest problems with AI to deliver social and economic value.
• Develop and maintain relationships with a broad range of clients, colleagues, and partners across a variety of contexts and formats.
• Build, lead and mentor a world-class team of AI and data engineers.
• Maintain a culture of excellence and lead with confidence, charisma, context, and humility working effectively at all levels.
• Create and deliver technical blogs & thought leadership on AI.
• Invest continuously in building and extending your knowledge and skills.

Your Background

• Exceptional expertise in AI engineering, machine learning and data science.
• Strong hands-on technical and coding skills are a strong advantage for this role.
• 7 years of experience delivering and managing AI solutions through incubation, proofs-of-concept, to deployment and commercialisation.
• Proven experience designing and deploying agentic AI systems — including multi-agent orchestration, tool-use, retrieval-augmented generation (RAG), and autonomous task execution frameworks (e.g. LangGraph, AutoGen, CrewAI, or similar).
• Candidates should demonstrate strength in either AI Engineering or ML Engineering (see below). Strength in both is a plus.

Platform & Delivery

• At least 5 years of implementation experience with serverless computing, CI/CD, containerisation, Infrastructure as Code, code version control and automated testing.
• Broad experience of model deployment, canary releases and implementation on cloud.
• Experience of automated data pipelines, data labelling, versioning and exploration.
• Strong ability to develop and maintain relationships amongst clients, colleagues, and partners.
• Ability to develop and deliver client proposals, and build consensus supported by detailed analysis, deep expertise and effective communication.
• Demonstrated ability to guide, develop and mentor AI and data engineers.
• Demonstrated ability to create technical blogs & thought leadership on AI.
• Active engagement in the AI community taking and giving courses, following podcasts, reading books, attending meetups, and developing projects.
• Familiarity with agentic coding tools such as Claude Code, Cursor and Google Antigravity will be an advantage.

You should demonstrate strong proficiency in either AI Engineering or ML Engineering / MLOps. Proficiency in both is a significant advantage.

AI Engineering (LLM & Agentic Systems)

• Practical hands-on experience with LLMs and agentic tooling: LangChain, LangGraph, AutoGen, CrewAI, OpenAI API, Anthropic API, AWS Bedrock, Google Vertex AI, Azure ML, Hugging Face Transformers, MLFlow, Dataiku, MS Fairlearn, Google PAIR.
• Experience with prompt engineering, fine-tuning, evaluation frameworks, and responsible AI tooling.

ML Engineering / MLOps

• Practical hands-on experience with ML development and deployment tooling: Jupyter, PyTorch, TensorFlow, Scikit-learn, AWS SageMaker, MLFlow, Docker, Kubernetes, Terraform, Ansible, Datadog.
• Broad experience of NLP, computer vision, classification & recommendation systems, reinforcement learning and time series.
• Experience designing and managing model experiment tracking and training workflows: hyperparameter tuning, cross-validation, experiment logging (e.g. MLFlow, Weights & Biases), and reproducible training runs.
• Solid understanding and hands-on experience with the MLOps lifecycle: data versioning, model training pipelines, experiment tracking, model registry, deployment, monitoring, drift detection, and retraining triggers — using platforms such as MLFlow, Weights & Biases, Kubeflow, AWS SageMaker Pipelines, Azure ML Pipelines, or Google Vertex AI Pipelines.