Head of Engineering

In this role you lead engineering for ML and AI at Sainsbury’s, shaping platforms and production ML capabilities to deliver scalable, reliable solutions for millions of customers. You’ll partner with business and tech leaders to drive innovation, agile ways of working, and technical excellence across teams. You’ll scale ML platforms, ensure governance and responsible AI, and own the path to production for ML assets. This is a high-impact, leadership role in a fast-moving, data-driven retail environment with a focus on delivery speed and engineering discipline.

• colleague discount (10% after 4 weeks)
• pensions scheme and life cover
• bonus potential up to 65% of salary
• annual holiday allowance with option to buy more
• cycle to work and other health/discount programs
• private healthcare and employee assistance programme

• Build and mentor engineering teams to deliver differentiating ML-enabled products
• Lead architecture and engineering standards across platforms and delivery
• Own end-to-end production readiness and deployment processes
• Collaborate with data scientists and business stakeholders to align priorities and outcomes
• Drive continuous improvement and cross-team alignment on technical excellence and delivery speed
• Manage relationships with senior leadership, partners, and suppliers to enable rapid scaling

• 10+ years in software/data engineering
• 4+ years in senior engineering leadership
• Proven experience building and scaling ML platforms (training, serving, feature stores, experimentation/model monitoring)
• Hands-on depth in distributed data systems
• Knowledge of modern AI infrastructure (LLM gateways, vector/semantic search, RAG pipelines, agentic frameworks)
• Track record of setting technical strategy and architectural decisions across multiple teams
• Strong stakeholder management to translate between data science, product, and executives
• Experience with cloud infrastructure and MLOps tooling
• Experience operating AI gateways or LLM orchestration layers in production

• strategic leadership
• stakeholder management
• agile mindset
• ML platforms and production ML
• distributed data systems
• AI infrastructure (LLM gateways, vector/semantic search, RAG pipelines)