Job Title: Lead Consultant - Knowledge graph expert

Introduction to role:

Are you ready to turn knowledge graphs and agentic AI into production products that accelerate how medicines reach patients? Do you thrive on moving from idea to working software, shipping secure, reliable systems that reason, plan and act across complex data and tools?

In this hands-on role, you will lead the design and build of GenAI assistants and agentic solutions that help global supply chain teams plan, source and deliver with greater speed and confidence. You will create end-to-end applications that combine RAG and Graph RAG with full-stack engineering and deploy them on AWS—bridging pioneering AI with the realities of enterprise operations to deliver measurable impact.

You will join a small, multi-functional team where product, data science and engineering sit side by side. Here, you will turn high-value supply chain problems into scalable AI products, moving quickly from PoC to production while building reusable patterns the wider organization can adopt.

Accountabilities:

• Product Strategy and Alignment: Translate priority supply chain problems into product roadmaps and technical strategies that deliver measurable outcomes (cycle-time reduction, service-level improvements, transparency for decision-makers).
• Agentic AI Development: Design and implement agents that plans, reason and act using tool/function calling and orchestration frameworks (e.g., LangGraph, CrewAI, AutoGen or Bedrock Agents), including multi-agent workflows where they contribute.
• End-to-End LLM Applications: Build robust RAG and Graph RAG pipelines—embeddings, vector search, retrieval and re-ranking, prompt orchestration and evaluation—to ground responses in trustworthy data and supply chain context.
• Knowledge Graph Integration: Model supply chain entities and relationships; integrate graph-based retrieval to power reasoning and traceability across processes and data sources, enabling better root-cause analysis and proactive interventions.
• Full-Stack Engineering: Develop back-end services and APIs (Python/Fast-API or Node.js) and front-end applications (React/Next.js, TypeScript) that put AI insights and actions in the hands of users.
• Cloud Deployment and Operations: Ship to production on AWS (Bedrock, SageMaker, Lambda, ECS/EKS, Open-Search, S3), with security, observability and cost-awareness built in from day one.
• Move use cases from PoC to services that are resilient and monitored. Define SLIs and SLOs. Implement testing and CI/CD. Use data to guide improvements.
• Reusable Components and Standards: Codify common patterns, guardrails and libraries across teams to accelerate delivery and ensure consistent quality.
• Stake-holder Partnership: Work closely with product, engineering, data science and supply chain leaders to prioritize, pilot and scale solutions; communicate trade-offs and guide adoption of AI driven ways of working.

Essential Skills/Experience:

• Strong full-stack software engineering — front-end (React / Next.js, TypeScript) and back-end (Python / FastAPI or Node.js), building and shipping production APIs and apps.
• Hands-on experience building agentic AI solutions — LLM agents that use tool calling / function calling, planning / reasoning loops, and orchestration frameworks (e.g. LangGraph, Bedrock Agents); multi-agent design a plus.
• Hands-on GenAI / LLM experience: RAG (embeddings, vector databases, chunking, retrieval, re-ranking) and exposure to Graph RAG.
• Solid AWS experience (preferred cloud) — e.g. Bedrock, SageMaker, Lambda, containers, Open-Search.
• Comfortable across the full SDLC in Agile teams; good communication.
• Product/Project Management experience in Data, Analytics & AI solutions
• Bachelor's - degree in IT, Computer Science, Data Analytics, or related field.

Desirable Skills/Experience:

• Master's - Degree, M.B.A, or Ph.d in a relevant field
• Knowledge Graph experience with Neo4j or Amazon Neptune (graph modelling, Cypher / Gremlin) — a strong plus, not required; we'll help you upskill.
• LangChain / Langsmith, LLM evaluation and guardrails, MLOps / LLMOps and Observability.
• Prompt-engineering fluency and awareness of current GenAI tooling.
• Pharma / regulated-industry or large-enterprise experience.

Why AstraZeneca:

Join a global community of technologists and data experts working shoulder to shoulder with scientists and supply chain leaders to reshape how medicines are made and delivered. We are investing heavily in modern platforms and AI, pairing bold ambition with the support to experiment, learn fast and scale what works. Expect unexpected teams in the same room unleashing inventive ideas, a thoughtful culture that values kindness alongside drive, and clear line of sight from your code to patient outcomes. You will have room to own outcomes, tackle complex challenges and grow your craft while helping an entire enterprise become truly data-led.

Call to Action:

If you want to build agentic, graph-powered AI that ships to production and moves life-changing medicines faster to patients, we’d love to see how you’ll lead the way!

Date Posted

27-Jul-2026

Closing Date

10-Aug-2026

AstraZeneca embraces diversity and equality of opportunity.  We are committed to building an inclusive and diverse team representing all backgrounds, with as wide a range of perspectives as possible, and harnessing industry-leading skills.  We believe that the more inclusive we are, the better our work will be.  We welcome and consider applications to join our team from all qualified candidates, regardless of their characteristics.  We comply with all applicable laws and regulations on non-discrimination in employment (and recruitment), as well as work authorization and employment eligibility verification requirements.