Forward Deployed AI Engineer

We’re looking for a highly skilled Forward Deployed AI Engineer to join our Health, Wealth & Career (HWC) segment—where you’ll design and deliver cutting-edge AI solutions that solve complex, real-world business challenges and shape the future of work.

This is a hands-on, high-impact role where you’ll move beyond traditional development—partnering closely with stakeholders to translate ambiguous needs into scalable, AI-powered solutions that drive measurable value.

You’ll own the full lifecycle—from architecture and build through to deployment and adoption—embedding AI into real workflows, integrating enterprise systems, and ensuring solutions are secure, reliable, and truly user-centric.

Sitting close to the business, you’ll continuously refine solutions to maximise impact, efficiency, and adoption.

us?

• At WTW, you’ll be part of our Health, Wealth & Career (HWC) segment—where we use data, technology, and deep expertise to shape the future of work.
• You’ll collaborate across multiple lines of business—such as Retirement, Health & Benefits, and Workforce Strategy—gaining exposure to diverse challenges and opportunities.
• As the role evolves, you’ll also help deliver innovative, AI-driven solutions directly to external clients, turning complex problems into scalable outcomes.
• Together, we’re creating more resilient, high-performing organizations—enhancing wellbeing, enabling better decisions, and driving sustainable business success at scale.
• This is a hybrid role, with the flexibility of remote working alongside occasional time in the office to build strong relationships and collaborate effectively with stakeholders.

The Role This position is ideal for an experienced Staff Engineer, or Technical Lead with a strong enterprise engineering background and a passion for applying it to AI-enabled systems.

You’ll bring deep expertise across modern full-stack technologies (.NET, Azure, SQL, React/Angular), along with experience in distributed systems, observability, and AI tooling such as LLMs, retrieval pipelines, agentic workflows, and platforms such as Anthropic Claude.

designing and deploying AI agents, leveraging Model Context Protocol (MCP), and orchestrating enterprise-grade AI solutions will be highly valued.

Acting as a bridge between business and technology, you’ll work across product, data science, architecture, and engineering teams, mentoring others, resolving production challenges, and scaling prototypes into robust, enterprise-grade solutions that deliver measurable business impact.

• AI solution delivery: Design and build AI-enabled applications, copilots, agents, extraction pipelines, prediction interfaces, and decision-support tools using foundation models, retrieval-augmented generation, structured outputs, and orchestration frameworks.
• Forward deployed problem solving: Work directly with business teams, product owners, clients, or operational users to understand real workflows, constraints, data quality issues, and adoption barriers, then translate these into working technical solutions.
• LLM and agent engineering: Build and tune LLM workflows, prompt strategies, schema-driven extraction, tool-calling patterns, agent orchestration, evaluation loops, and human-in-the-loop controls.
• Enterprise integration: Integrate AI solutions with enterprise systems, APIs, data platforms, document repositories, workflow tools, observability platforms, and identity and access management services.
• Production engineering: Ensure AI solutions meet enterprise standards for reliability, scalability, latency, maintainability, cost control, logging, monitoring, and operational support.
• Evaluation and quality assurance: Create evaluation datasets, test harnesses, validation tools, regression checks, and quality review workflows to measure accuracy, extraction quality, hallucination risk, and business usefulness.
• Architecture and technical leadership: Define solution architecture, engineering standards, reusable patterns, and implementation approaches for AI-enabled platforms and services.
• Data and knowledge readiness: Work with engineering, data, and business teams to prepare structured and unstructured data, improve metadata, design retrieval strategies, and identify gaps in source content.
• Security, privacy, and governance: Embed access controls, audit logging, data protection, responsible AI controls, security review, and compliance requirements into the AI delivery lifecycle.
• Adoption and enablement: Support users through demos, pilots, training, feedback loops, documentation, and iterative improvement so that deployed AI solutions create measurable business value.