Job Title
Forward Deployed Engineer, Manager (UK)
Location
London
Salary
Competitive
Date
9 Apr 2026
Job Description
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.
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 lead the technical delivery of AI-enabled solution threads within client engagements. You will guide teams in designing robust AI architectures, ensure engineering quality across delivery, and help translate complex business problems into reliable systems operating in production environments.
You will work at the intersection of software engineering, product thinking and client delivery, shaping technical approaches and supporting squads in delivering high‑quality solutions.
In this Manager role within the FDE team, you will act as a technical leader within delivery teams, shaping solutions, raising engineering standards and guiding teams to build AI-enabled systems that operate reliably within complex enterprise environments.
Responsibilities
Client‑Facing Engineering & Delivery
- Lead technical delivery for AI solution areas, guiding teams in translating client needs into scalable engineering approaches.
- Engage with business and technology stakeholders to shape technical direction, communicate trade‑offs and ensure alignment on solution outcomes.
- Support delivery teams in navigating complex client environments while ensuring engineering quality and reliability.
Solution Design & Implementation
- Architect AI-enabled services such as agents, RAG pipelines and supporting platform components.
- Ensure solutions are designed with reliability, observability and operational readiness in mind.
- Guide teams in implementing responsible‑AI controls, evaluation approaches and engineering best practices.
Product Mindset & Continuous Improvement
- Mentor engineers and support the development of strong engineering practices across squads.
- Lead technical reviews and help establish reusable patterns, accelerators and reference architectures.
- Contribute to internal knowledge sharing and external thought leadership around applied AI engineering.
Qualifications
Essential Skills & Experience
- Software & systems engineering: Python/TypeScript, distributed systems, API/microservice design, testing/CI/CD.
- Applied AI/ML: building and operating ML/DL in production; expertise in NLP/CV/transformers and classical ML.
- LLM/RAG engineering: embeddings, vector stores (FAISS/Milvus/Pinecone), retrieval strategies, grounding and hallucination mitigation.
- LLMOps: prompt pipelines, automated evaluation, telemetry/drift monitoring, model versioning and release management.
- Cloud architecture: Azure (preferred) and/or AWS/GCP; Kubernetes/Docker; serverless; IAM and network security.
- Data engineering: Spark/Databricks, ETL/ELT; collaboration with platform/data teams to deliver cloud-native data + AI architectures.
- Enterprise integration: legacy/LoB systems; design for reliability/observability (SLIs/SLOs) and operational readiness with runbooks/SRE practices.
- Product leadership: discovery facilitation, PRDs, acceptance criteria, prioritisation (RICE/MoSCoW), value/adoption metrics.
- Responsible AI & compliance: privacy‑by‑design, auditability and UK regulatory awareness (FCA, PRA, GDPR).
- Consulting capabilities: stakeholder management, client‑ready communication, time/budget/risk management and team leadership.
Nice to Have
- Big‑data/graph stacks (e.g., Hadoop, Cassandra, Neo4j) and streaming (Event Hub/Kafka).
- Azure/AWS Solutions Architect experience; optional governance/model‑risk/responsible‑AI credentials.
Technical Certifications (Preferred)
- Microsoft Azure AI Engineer Associate (AI‑102) or Azure Data Scientist Associate.
- AWS Machine Learning Specialty or Google Professional ML Engineer.
- Databricks (Data Engineer/ML Engineer) and Kubernetes (CKA/CKAD).
- Azure/AWS Solutions Architect; optional model‑risk/responsible‑AI governance credentials.
How You Work
- You are hands‑on with engineering while setting the technical direction for delivery teams.
- You help teams navigate technical trade‑offs and ensure solutions meet enterprise standards for reliability and security.
- You care about quality, operational readiness and long‑term maintainability of systems delivered to clients.
What We Offer
High‑impact work with leading organisations across sectors within a collaborative engineering‑led AI capability.
You Will Benefit From
- Continuous development through the FDE Academy, strengthening architecture and engineering leadership capabilities required to build AI systems at scale.
- Opportunities to participate in hackathons, engineering showcases and innovation challenges.
- Learning and certification support across cloud, AI and engineering platforms.
- 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.
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