Forward-Deployed AI Engineer for Client Delivery

At EY, we’re all in to shape your future with confidence. We’ll help you succeed in a globally connected powerhouse of diverse teams and take your career wherever you want it to go. Join EY and help to build a better working world.

EY is investing in engineering-led AI at scale. EY has been selected for the inaugural Frontier Firm AI Initiative — a collaboration between Microsoft and Harvard’s Digital Data Design Institute — recognising EY’s leadership in shaping enterprise-grade human–AI operating models. In parallel, EY is expanding its collaboration with OpenAI and Microsoft, bringing advanced AI capabilities to clients through Microsoft’s secure Azure OpenAI Service. Joining this team means being part of a growing engineering capability focused on building production-grade AI systems for major organisations across sectors.

The opportunity: Organisations are moving rapidly from AI experimentation to operational adoption. However, many struggle to translate ideas into secure, scalable and reliable production solutions. As a Forward Deployed Engineer, you will sit at the intersection of software engineering, product thinking, and client delivery, working within a squad to build real AI-powered solutions. You will collaborate with engineers, designers and client teams to turn complex problems into reliable systems.

In this consultant role within the Forward Deployed Engineering team, you will develop foundational AI engineering and product skills within a delivery squad. You will learn how to design, build and integrate LLM/RAG features, contribute to discovery and user stories, and follow EY standards for quality, security and documentation while supporting demos and adoption.

What you’ll do:

• Client-facing engineering & delivery: Support discovery workshops by capturing requirements and helping translate them into user stories and acceptance criteria. Support demos, show‑and‑tells and feedback cycles; prioritise fixes and enhancements with the squad. Work effectively in diverse, multidisciplinary teams and produce client-ready artefacts to agreed timelines.
• Solution design & implementation: Build LLM/RAG features and integrations under guidance, contributing clean, tested, maintainable code. Follow EY reference architectures, security patterns and evaluation baselines, contributing to documentation and reusable accelerators.
• Product mindset & continuous improvement: Contribute to documentation and reusable components/accelerators that help teams deliver consistently. Learn field lessons (what works in client environments) and feed that back through the squad’s delivery routines and artefacts.

What we’re looking for:

• Essential skills & experience: Software engineering fundamentals (algorithms, data structures, APIs, microservice basics). Programming in Python and/or TypeScript; exposure to async patterns, testing, and version control (Git). LLM/RAG basics: embeddings, prompt design, retrieval patterns; willingness to learn evaluation and guardrails. Intro experience with data wrangling for structured and unstructured data; feature engineering fundamentals. Cloud fundamentals (Azure preferred); containers (Docker) and CI/CD exposure. Understanding of responsible‑AI principles, privacy and basic model‑risk concepts; eagerness to learn UK regulatory context (FCA, PRA, GDPR). Clear written and verbal communication; ability to operate in client environments and collaborate with security, risk and architecture teams. Growth mindset: curiosity, continuous learning, and ability to adapt quickly to new techniques and tools.
• Nice to have: Familiarity with ML concepts (regression, classification, clustering) and deep‑learning frameworks (PyTorch/TensorFlow). Foundational knowledge of data platforms (Spark/Databricks) and event/streaming patterns (e.g., Event Hub/Kafka). Technical Certifications (preferred) Microsoft Azure AI Engineer Associate (AI‑102) — in progress or planned. Azure Data Scientist Associate — in progress or planned. AWS Machine Learning Specialty or Google Professional ML Engineer — welcome. Databricks and Kubernetes (CKA/CKAD) — welcome for future development.

How you work:

• You’re comfortable being hands-on: coding, debugging, deploying, and iterating with users.
• You care about quality—but you’re pragmatic about what’s “good enough” to ship and improve safely, and you’re eager to learn from feedback.
• You can communicate clearly with both technical and non-technical stakeholders as you build confidence in client settings.

What we offer:

• High-impact work with leading organisations across sectors, within a collaborative engineering-led AI team.
• You will benefit from a structured FDE Academy and cohort learning experience.
• Opportunities to participate in hackathons, innovation challenges and engineering showcases.
• Learning and certification support across cloud and AI technologies.
• Competitive compensation and benefits.
• Flexible hybrid working arrangements depending on client needs.

Travel & Working Model:

Hybrid working and periodic travel to client sites across the UK (and occasionally internationally), discussed based on projects and location.

Inclusion and accessibility:

EY is committed to building an inclusive culture where everyone can thrive. If you require adjustments or support during the recruitment process, we encourage you to let us know.

EY | Building a better working world. EY is building a better working world by creating new value for clients, people, society and the planet, while building trust in capital markets. Enabled by data, AI and advanced technology, EY teams help clients shape the future with confidence and develop answers for the most pressing issues of today and tomorrow. EY teams work across a full spectrum of services in assurance, consulting, tax, strategy and transactions. Fueled by sector insights, a globally connected, multi-disciplinary network and diverse ecosystem partners, EY teams can provide services in more than 150 countries and territories.