Senior AI Engineer

dunnhumby is the global leader in Customer Data Science, partnering with the world’s most ambitious retailers and brands to put the customer at the heart of every decision. We combine deep insight, advanced technology, and close collaboration to help our clients grow, innovate, and deliver measurable value for their customers. dunnhumby employs nearly 2,500 experts in offices throughout Europe, Asia, Africa, and the Americas working for transformative, iconic brands such as Tesco, Coca-Cola, Nestlé, Unilever and Metro. 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. Key Responsibilities 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. Write clean, tested code; drive architecture reviews, code reviews, and mentor engineers. Required Qualifications Bachelor's/Master's in Computer Science, AI, Engineering, or related field. 8+ years of software engineering experience, including 3+ years building production AI/ML applications. Strong grounding in distributed systems, cloud-native architecture, and microservices. Track record delivering enterprise-grade AI solutions from POC to production. Technical Skills Programming: Python (expert), TypeScript/JavaScript, SQL, async programming, REST & gRPC, design patterns AI/ML Concepts: LLMs, prompt engineering, embeddings, RAG, hybrid search, agentic AI (tool/function calling, MCP, A2A), model training & fine-tuning (LoRA/QLoRA/PEFT), hyperparameter optimization, GPU optimization, quantization AI Frameworks: LangChain, LangGraph, Google ADK, OpenAI Agents SDK, CrewAI, AutoGen, LlamaIndex, Semantic Kernel, DSPy, Pydantic AI Cloud AI Platforms: Google Vertex AI, Azure AI Foundry, Azure OpenAI, Model Garden ML & MLOps: PyTorch, TensorFlow, Hugging Face, MLflow, Kubeflow, model registry, experiment tracking, continuous training AgentOps & Observability: LangSmith/Langfuse/Arize Phoenix, OpenTelemetry, Grafana, New Relic, agent evaluation, cost monitoring Vector Databases: Pinecone/Weaviate/Milvus/pgvector/Vertex AI Vector Search/Azure AI Search Cloud & DevOps: Docker, Kubernetes, Terraform, ArgoCD, GitHub Actions/Azure DevOps, CI/CD, IaC Workflow Orchestration: Temporal/Argo Workflows/event-driven architecture/message queues What you can expect from us We won’t just meet you…