Artificial intelligence Developer (Pro Code)

Willis Towers Watson is a leading global advisory, broking and solutions company that helps clients around the world turn risk into a path for growth.

With roots dating to 1828, Willis Towers Watson has 45,000 employees serving more than 140 countries.

We design and deliver solutions that manage risk, optimize benefits, cultivate talent, and expand the power of capital to protect and strengthen institutions and individuals.

Our unique perspective allows us to see the critical intersections between talent, assets and ideas – the dynamic formula that drives business performance.

Together, we unlock potential.

Learn more at willistowerswatson.com.

The AI/M365 team is a key part of WTW Corporate Platform’s Technology function, focused on delivering AI-first, automation, analytics, and modern digital solutions that drive efficiency, innovation, and collaboration across the enterprise. Our globally distributed team comprises AI engineers, cloud specialists, analytics professionals, and Microsoft 365 experts who leverage technologies such as Azure, OpenAI, Gemini, and Model Context Protocol (MCP) to develop intelligent applications, generate data-driven insights, and implement scalable automation solutions. As demand for AI-powered capabilities and analytics-driven decision-making continues to grow, we are expanding our team to accelerate the delivery of high-impact, scalable solutions that support business transformation and create value across the organization.

We are looking for a pro-code AI (Artificial intelligence) Developer who builds production-grade AI (Artificial intelligence) systems in code. You will design and build AI (Artificial intelligence) agents, RAG (Retrieval-Augmented Generation) pipelines, and AI-powered (Artificial intelligence) applications primarily in Python — orchestrating LLMs (Large Language Models.)with frameworks such as LangChain, LangGraph, and AutoGen, integrating them into enterprise systems via APIs(Application Programming Interface) and MCP(Model Context Protocol), and deploying them on the cloud (including Azure AI (Artificial intelligence) Foundry). The ideal candidate has a strong software-engineering foundation, a strong bias to build, and can move from a loosely defined idea to a working proof-of-concept in days, not weeks.

Design and build AI (Artificial intelligence) agents, RAG (Retrieval-Augmented Generation) pipelines, and AI-powered (Artificial intelligence) applications in code (primarily Python), orchestrating LLMs(Large Language Models.) with frameworks such as LangChain, LangGraph, and AutoGen.

Implement advanced RAG (Retrieval-Augmented Generation) techniques — hybrid search, re-ranking, contextual chunking, and long-context strategies — and agentic orchestration patterns (ReAct, plan-and-execute, multi-agent).

Build and integrate APIs(Application Programming Interface) and enterprise connectors, including MCP-based integrations, to connect AI (Artificial intelligence) capabilities with enterprise systems and data sources.

Deploy, configure, and operate AI (Artificial intelligence) workloads on the cloud, owning solutions end-to-end from prototype to production.

Design AI (Artificial intelligence) solutions that scale — accounting for performance, throughput, latency, cost, and reliability as usage grows from POC (Proof of Concept) to enterprise scale.

Rapidly prototype: take a loosely defined idea or business problem and produce a working proof-of-concept quickly, then iterate based on feedback.

Apply sound engineering judgment — error handling, evaluation, observability, security, and access considerations — across the solutions you build.

Strong hands-on software development experience in Python, with a solid engineering foundation (you can design, write, test, and debug production code).

Hands-on experience with LangChain, LangGraph, and/or AutoGen, and with agentic orchestration patterns for building multi-agent or agentic AI (Artificial intelligence) systems.

Proven experience building RAG (Retrieval-Augmented Generation) systems, including advanced techniques such as hybrid search, re-ranking, contextual chunking, and long-context strategies.

designing and consuming APIs(Application Programming Interface) and integrations; familiarity with MCP (Model Context Protocol) as an emerging integration pattern.

Strong, hands-on command of cloud concepts on at least one major provider (Azure preferred; AWS or GCP considered) — able to independently provision, configure, develop against, deploy, and operate AI (Artificial intelligence) workloads and cloud resources.

Hands-on experience with Azure AI (Artificial intelligence) Foundry (or an equivalent managed AI (Artificial intelligence) platform).

Understanding of how to design and scale AI (Artificial intelligence) solutions for production — performance, throughput, latency, cost optimization, and reliability — including patterns such as caching, asynchronous processing, load handling, and horizontal scaling.

Demonstrated ability to rapidly prototype — translating an idea into a functional proof-of-concept quickly using AI-assisted (Artificial intelligence) development and fast iteration.

Experience with AI (Artificial intelligence) evaluation and observability tooling (e.g., ragas, LangSmith, Promptflow evals, Azure Monitor).

Familiarity with AI (Artificial intelligence) safety, responsible AI principles, and enterprise guardrail patterns (content filtering, grounding checks).

Working knowledge of low-code AI (Artificial intelligence) platforms (Microsoft Copilot Studio, Power Platform) or agent-builder platforms (Lyzr, Moveworks) for rapid delivery where they are the right fit.

integrating with Microsoft 365 and Dataverse, or an equivalent enterprise ecosystem.

You are first and foremost a strong engineer who has moved into AI (Artificial intelligence) — comfortable living in code, reasoning about architecture, and owning a solution from prototype to production.

You reach for low-code tools when they are genuinely the fastest path, but your default is to build in code, and you would rather ship a rough proof-of-concept today than a perfect specification next month.

• Stage 1: Online application and recruiter review
• Stage 2: Pre-recorded video interview
• Stage 3: Live video or in person interview with hiring manager and team
• Stage 4: Offer and onboarding