Forward Deployed AI Engineer

Overview

In this hybrid, high-impact role you will design and deliver AI-powered solutions for the Health, Wealth & Career (HWC) segment, translating complex business needs into scalable deployments. You work closely with product, data science, architecture, and engineering teams to embed AI into real workflows and enterprise systems. You own end-to-end delivery from architecture to adoption, focusing on measurable business value and user-centric outcomes. This role offers cross-functional collaboration, a chance to influence large-scale AI initiatives, and direct client impact.

Pay / Benefits

• 25 days of annual leave
• private healthcare
• pension with matched contributions up to 10%
• hybrid working
• volunteer day
• share scheme

Responsibilities

• Design and build AI-enabled applications, copilots, agents, extraction pipelines, and decision-support tools
• Translate business workflows and constraints into deployable AI solutions across enterprise systems
• Develop and tune LLM workflows, prompt strategies, and agent orchestration with human-in-the-loop controls
• Integrate AI solutions with APIs, data platforms, document repositories, and observability/security tooling
• Ensure production-quality delivery with reliability, latency, cost controls, and observability
• Create evaluation datasets, test harnesses, and QA workflows to measure accuracy and usefulness
• Define solution architecture, engineering standards, and reusable patterns for AI platforms
• Collaborate with engineering, data, and business teams to prepare data and metadata for retrieval and knowledge readiness
• Embed security, privacy, governance, and compliance into the AI delivery lifecycle
• Support adoption through demos, pilots, trainings, and documentation
• Mentor engineers and influence technical delivery beyond direct reports

Key requirements

• Significant professional experience in software engineering, technical leadership, or platform engineering
• Proven ability to deliver production applications using APIs, distributed systems, microservices, CI/CD, and cloud platforms
• Hands-on experience building AI-enabled systems (LLM pipelines, extraction, structured outputs, or agentic workflows)
• Experience with business-critical systems requiring reliability, maintainability, and operational support
• Experience collaborating with data scientists, product managers, QA, architects, security teams, and senior stakeholders
• Track record of mentoring engineers and establishing engineering standards

• Cross-functional collaboration
• Mentoring and technical leadership
• Stakeholder engagement
• .NET
• Azure
• SQL

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