AI / GenAI Engineering Lead

Role Description

ZS is a place where passion changes lives. As a management consulting and technology firm focused on improving life and how we live it, we transform ideas into impact by bringing together data, science, technology and human ingenuity to deliver better outcomes for all. Here you’ll work side-by-side with a powerful collective of thinkers and experts shaping life-changing solutions for patients, caregivers and consumers, worldwide. ZSers drive impact by bringing a client-first mentality to each and every engagement. We partner collaboratively with our clients to develop custom solutions and technology products that create value and deliver company results across critical areas of their business. Bring your curiosity for learning, bold ideas, courage and passion to drive life-changing impact to ZS.

What You’ll Do

As an AI / GenAI Engineering Lead within ZS EDGE Technology in Poland, you will design, build and take to production enterprise-grade generative AI and agentic AI solutions.

This is a senior, hands‑on engineering role at the intersection of agentic AI and software engineering. You will develop systems that combine large language models, enterprise data, tools, APIs and business workflows, taking solutions from initial design and proof of concept through production deployment, testing, monitoring and ongoing support.

You will work with engineering, architecture, advisory and account teams across Europe to translate emerging client needs into reliable, secure and scalable AI solutions. Although the initial demand is aligned to a major life‑sciences client, the engineering patterns and capabilities developed through this role should be reusable across wider ZS AI delivery.

Your responsibilities will include:

• Design, build and deploy production‑grade GenAI and agentic AI applications
• Develop multi‑agent systems covering orchestration, routing, delegation, tool calling, state management, memory and human‑in‑the‑loop patterns
• Build agentic workflows using frameworks such as LangGraph, LangChain or equivalent orchestration technologies
• Develop and deploy AI workloads on AWS using relevant generative AI and cloud‑native services
• Integrate AI agents with enterprise APIs, applications, structured and unstructured data and business workflows
• Build retrieval‑augmented generation and enterprise knowledge‑retrieval capabilities
• Design robust agent workflows with retries, fallback logic, checkpointing, error handling and recovery
• Build evaluation and automated‑testing approaches covering task completion, tool‑use accuracy, groundedness, quality and regression
• Apply sound software‑engineering practices, including modular design, code review, version control and automated testing
• Establish deployment practices using containerisation, CI/CD, infrastructure as code, monitoring and observability
• Implement security, access, traceability and audit controls appropriate for enterprise and regulated environments
• Monitor agent behaviour, workflow execution, latency, reliability, quality and operating cost
• Contribute to high‑level and low‑level solution design and technical architecture reviews
• Define reusable agentic architecture patterns, reference implementations and engineering standards
• Collaborate with technical and non‑technical stakeholders to turn ambiguous requirements into deliverable solutions
• Participate, where required, in short European client engagements

What You’ll Bring

• Significant hands‑on experience in software engineering, AI engineering, machine learning engineering or generative AI delivery
• Strong Python development skills and sound software‑engineering fundamentals
• Demonstrable experience building GenAI or agentic AI applications beyond isolated prototypes
• Experience designing complex agentic workflows or multi‑agent systems
• Practical understanding of tool and function calling, stateful workflows, agent routing, delegation, memory and context management
• Experience using agent orchestration frameworks such as LangGraph, LangChain or comparable technologies
• Experience integrating AI applications with enterprise APIs, databases, systems and external tools
• Experience building RAG or enterprise knowledge‑retrieval solutions
• Understanding of embeddings, vector search, document processing and grounding
• Experience defining test cases and evaluating agentic or LLM‑based applications
• Understanding of production‑quality engineering, including Docker, CI/CD, automated testing, logs, monitoring and observability
• Experience deploying and operating cloud‑based applications
• Solid SQL, API, data‑pipeline and structured/unstructured data‑processing foundati