AI Engineering Manager

Tesco Media & Insight Platform is a partnership between Tesco, the UK’s largest grocery retailer, and dunnhumby. Fueled by first‑party behavioral data from over 23 million Clubcard holders, we help brands understand consumer behaviors and reach the right customers, at the right time, with the right message across the full marketing funnel. Our closed‑loop measurement connects what customers see with what they buy, helping brands Shape What Britain Buys and maximize the impact of every pound of media investment.

The Roles

As an AI Engineering Manager, you will lead a team of AI engineers to design, build and operate production‑grade GenAI and agentic AI solutions that power Tesco Media & Insight Platform’s global products and services from our India hub. You will combine deep hands‑on AI/ML and cloud engineering experience with people leadership and stakeholder management, ensuring that our AI capabilities are secure, scalable, observable and deliver measurable value for brands and shoppers.

What you’ll be doing

• Lead a high‑performing team of AI engineers in India to deliver secure, scalable AI and agentic solutions into production.
• Run structured intake for new AI opportunities, working with Product, Commercial, Data Science and Engineering teams in India and the UK to clarify business problems, define scope and success metrics, and shape roadmaps.
• Architect and implement GenAI and agentic AI solutions on GCP, leveraging Vertex AI (models, endpoints, pipelines, vector search/RAG, Agent Builder) and core GCP services.
• Use Microsoft AI / Azure AI Foundry (or equivalent) and modern agent platforms to design and implement agentic workflows, tools and integrations with internal and external systems.
• Design AI agents and multi‑step agentic workflows, including tool calling, MCP servers and agent‑to‑agent protocols, to automate and augment retail media and insight use cases.
• Ensure security, privacy and governance across AI solutions, including handling of sensitive data, access control, secrets management and safety/guardrail mechanisms.
• Define and embed DevOps/MLOps practices for AI (CI/CD for models, prompts and agents; infrastructure as code; environment promotion; blue/green and canary releases; incident response).
• Implement robust observability for AI systems: tracing prompts and tool calls, logging, metrics, dashboards and alerts to monitor quality, performance and cost.
• Establish AI testing and evaluation strategies: offline and online model evaluation, red‑teaming, regression testing, and human‑in‑the‑loop review and feedback loops.
• Partner with Retail Media and Insight teams to identify and deliver AI capabilities across the media lifecycle – including, but not limited to, audience and insight generation, campaign planning and optimization, creative support, measurement and attribution, workflow automation, and decision support for internal and external users.
• Act as a hands‑on technical leader, reviewing designs and code, unblocking complex problems, and setting engineering standards and best practices for AI delivery.
• Coach, mentor and develop AI engineers, supporting their growth and ensuring a culture of learning, experimentation and continuous improvement.

What you’ll need

• Strong experience in software/data/ML engineering, including significant hands‑on work building and operating AI/ML or LLM‑based solutions in production.
• Proven experience leading AI/ML/LLM engineering teams or acting as a senior technical lead, ideally in a global or distributed setup.
• Deep practical knowledge of GCP and Vertex AI, with experience designing and operating production workloads using Vertex AI models, endpoints, pipelines and RAG/vector search.
• Experience with Microsoft AI / Azure AI Foundry or similar platforms, and familiarity with at least one modern agentic AI framework or platform.
• Solid understanding of LLMs and foundation models (transformers, tokenization, context windows, embeddings, RAG, fine‑tuning vs adapters vs prompts, tool calling and risks such as hallucinations or prompt injection).
• Hands‑on experience designing and building AI agents or agentic workflows, integrating them with tools, APIs and internal systems, and managing their reliability, safety and observability.
• Strong background in security, governance and compliance for AI solutions, including handling of sensitive data, role‑based access controls and auditability.
• Experience with DevOps/MLOps practices for AI: CI/CD, model and prompt versioning, environment management, infrastructure as code, monitoring and incident management.
• Knowledge of AI evaluation techniques and model testing, including experiment design, quantitative and qualitative evaluation, and human‑in‑the‑loop processes.
• Excellent communication and stakeholder management skills; able to collaborate effectively with teams in India and internationally, and to balance ambition, feasibility and risk.