AI Engineer

About dunnhumby

dunnhumby is the global leader in Customer Data Science, helping the world's most ambitious retailers and brands put the customer at the heart of every decision. With nearly 3,000 experts across Europe, Asia, Africa and the Americas, we partner with iconic businesses like Tesco, Coca-Cola, Meijer, Procter & Gamble and Metro to turn data into growth, innovation and measurable value for their customers.

Tech / Engineering

dunnhumby’s Technology and Engineering team builds the platforms, pipelines and systems that make customer data science possible at scale. From the data infrastructure that ingests billions of transactions to the APIs and cloud architecture that power our products, we engineer reliable, high-performance software trusted by the world’s leading retailers and brands.

We're looking for a Senior AI Engineer to help build and scale dunnhumby's Enterprise AI Platform- designing, deploying, and operating production grade AI systems used across engineering teams. You'll work across the full AI lifecycle: model training and fine-tuning, agentic workflows, RAG, AI observability, and AI-powered user experiences, using the latest advancements in Generative AI.

What You'll Do

• Build reusable, scalable AI services for prompt orchestration, model routing, embeddings, structured generation, and tool calling; develop configurable multi-provider AI runtimes and secure cloud-native microservices.
• Design multi-agent and autonomous systems with reasoning, planning, memory, and tool execution; build graph-based, long-running workflows with human-in-the-loop checkpoints using MCP and A2A.
• Build enterprise-grade RAG pipelines— ingestion, chunking, embeddings, hybrid search, reranking, citations — and continuously evaluate retrieval quality.
• Train, fine tune (LoRA/QLoRA/PEFT), and evaluate ML/DL models; build training pipelines, run experimentation and hyperparameter optimization, and productionize models with data science partners.
• Deploy, monitor, and continuously improve agents and models in production — experiment tracking, model registry, versioning/rollback, drift and cost monitoring, CI/CD, and canary/blue-green deployments.
• Implement guardrails for hallucination, prompt injection, and PII; establish evaluation, monitoring, and responsible-AI compliance practices.
• Build and deploy cloud-native AI services (Docker, Kubernetes, Terraform) on GCP and Azure with autoscaling, observability, and distributed tracing; own services from build through production support.
• Build responsive React-based interfaces for chat, copilots, prompt playgrounds, and agent/evaluation dashboards, integrated via REST, SSE, and WebSockets.