Delivery Lead

Data Science & AI Delivery Lead

London

Hybrid – minimum 3 days per week in the local office

£90,000–£120,000 base + 15–20% target bonus

We are looking for an experienced Data Science & AI Delivery Lead to join a major international organisation investing significantly in Data, AI and advanced analytics.

This is a senior, hands-on technical leadership position for someone who can combine AI/ML engineering expertise with delivery leadership. You’ll act as the deputy to the Head of Data Science & AI Delivery, taking responsibility for the day-to-day execution of AI initiatives and helping a growing team take solutions from concept and proof of value through to scalable production deployments.

The organisation sees Data and AI as a major strategic capability – using information to make better decisions, identify opportunities earlier, improve operational efficiency and create competitive advantage.

Importantly, this is not a purely managerial role. You’ll remain close to the technology, working directly with the codebase, reviewing technical designs and architecture, establishing engineering standards and contributing hands-on when required.

You’ll take a leading role in the technical execution and delivery of AI, machine learning and advanced analytics solutions, including:

• Owning the day-to-day running of AI delivery workstreams, ensuring teams remain focused, unblocked and aligned to priorities.
• Acting as deputy to the Head of Data Science & AI Delivery and providing leadership continuity across projects and stakeholder forums.
• Taking technical delivery accountability from initial concept through development, deployment and production.
• Leading AI solution architecture, technical design reviews, implementation approaches and production-readiness assessments.
• Establishing engineering standards, reusable frameworks, patterns and best practices for AI delivery.
• Providing technical guidance around solution design, model selection and architecture.
• Remaining hands-on with Python development, particularly during critical delivery phases and proof-of-concept work.
• Reviewing code and technical outputs to maintain high engineering and quality standards.
• Designing and delivering Generative AI and LLM solutions, including RAG architectures, prompt engineering, vector search and integration with Azure AI services.
• Defining engineering approaches for LLM applications, agentic AI systems, machine learning solutions and AI platforms.
• Establishing strong MLOps practices covering model versioning, automated testing, deployment pipelines, monitoring, observability and model lifecycle management.
• Ensuring solutions meet appropriate security, governance, scalability, explainability and operational support requirements.
• Evaluating emerging AI technologies and determining where they can deliver meaningful business value.
• Working closely with architecture, technology, programme and Data Engineering teams to ensure effective end-to-end delivery.
• Supporting delivery planning, timelines, cost estimates, cloud/API consumption and resource allocation.
• Mentoring Data Scientists and AI Engineers through architecture reviews, code reviews, pair programming and technical coaching.
• Helping create a high-performing engineering culture focused on collaboration, quality and continuous learning.

You’ll need demonstrable experience delivering AI and machine learning solutions into production within a professional environment, alongside the technical credibility to lead experienced Data Scientists and AI Engineers.

Key experience includes:

• Expert-level Python development.
• Strong experience with core data science and machine learning libraries such as scikit-learn, pandas, PyTorch and/or TensorFlow.
• Practical experience with Databricks, including MLflow and Spark-based data processing.
• Hands-on knowledge of Generative AI and LLMs, including prompt engineering, RAG architectures and vector search.
• Strong understanding of MLOps, including CI/CD, model testing, deployment, monitoring and model lifecycle management.
• Cloud experience, ideally within the Microsoft Azure ecosystem.
• Experience with services such as Azure OpenAI, Azure Machine Learning and/or Azure AI Search.
• Experience leading technical delivery teams while remaining actively involved in engineering and delivery.
• Strong understanding of software engineering practices including Git, code review, documentation and the transition from experimentation to production-grade code.
• Excellent stakeholder communication skills, including the ability to translate complex technical concepts into clear business language.
• Strong delivery and project management capabilities, including resource estimation, progress tracking and managing competing priorities.

We’re particularly interested in people who combine technical depth with leadership ability – someone comfortable discussing strategy with senior stakeholders one moment and reviewing Python code or AI architecture with engineers the next.