Job title
Applied AI Engineering Squad Lead — Product & Engineering
Location & contract
Location: UK (London / Manchester / Birmingham / Edinburgh) — Hybrid working with client-site travel as required.
Contract: Permanent, full-time
What you’ll do
- Client-facing engineering & delivery
- Define the strategic direction for the squad, including roadmap priorities, solution scope and delivery outcomes.
- Partner with senior client stakeholders to shape AI solution vision, adoption strategies and value realisation.
- Drive delivery across complex programmes, managing dependencies, risks and delivery transparency.
- Solution design & implementation
- Lead the end-to-end delivery of AI-enabled systems, including agents, retrieval systems and supporting services.
- Ensure solutions align with enterprise architecture standards, responsible-AI requirements and operational readiness practices.
- Establish strong engineering ways-of-working across the squad, including review practices, reliability patterns and observability.
- Product mindset & continuous improvement
- Shape product thinking around applied AI solutions, helping teams translate opportunities into scalable solution designs.
- Mentor engineers and develop high-potential talent across the capability.
- Contribute to thought leadership and help represent EY’s Applied AI Engineering capability in market-facing initiatives.
What we’re looking for
Essential skills & experience
- Expert software and systems engineering: Python/TypeScript, distributed systems, API/microservice architecture and cloud‑native patterns.
- Deep applied AI/ML mastery: NLP/CV/transformers, generative models (GANs/VAEs), reinforcement learning, classical ML and statistical modelling.
- Advanced LLM/RAG engineering: prompt pipelines, embeddings, vector stores (FAISS/Milvus/Pinecone), hybrid retrieval, grounding, hallucination mitigation and evaluation frameworks.
- LLMOps/MLOps: automated testing, drift monitoring, safety/guardrails, CI/CD for ML, telemetry, lineage and governance.
- Cloud architecture leadership: Azure (preferred), AWS/GCP; Kubernetes/Docker; serverless; IAM, VNETs, zero‑trust patterns and secure network architecture.
- Data engineering architecture: Spark/Databricks, ETL/ELT frameworks; big‑data/graph stacks (Hadoop, Cassandra, Neo4j); streaming (Event Hub/Kafka).
- Enterprise integration: legacy/LOB systems, event workflows, case management platforms; design for high availability, resilience and observability.
- Product leadership: conducting discovery, framing hypotheses, shaping MVPs, backlog ownership, value/adoption metrics and client‑ready PRDs.
- Responsible AI & compliance: privacy‑by‑design, auditability, fairness and transparency; strong awareness of UK financial‑services regulatory context (FCA, PRA, GDPR).
- Consulting leadership: stakeholder management, commercial awareness, proposal shaping, solution positioning and creation of thought leadership.
- Demonstrated ability to lead multi‑disciplinary squads (engineering, data science, architecture, product, design) through complex delivery cycles.
Nice to have
- Optional: governance/model‑risk/responsible‑AI certifications.
Technical Certifications (preferred)
- Azure AI Engineer (AI‑102) or Azure Data Scientist Associate.
- AWS Machine Learning Specialty or Google Professional ML Engineer.
- Databricks Machine Learning Engineer, Kubernetes (CKA/CKAD).
- Azure/AWS Solutions Architect certifications.
- Optional: governance/model‑risk/responsible‑AI certifications.
How you work
- You’re hands-on when needed, but primarily you create the conditions for repeatable delivery: clear direction, strong ways-of-working, and high engineering standards.
- You earn trust with senior stakeholders by explaining trade-offs simply and steering delivery through ambiguity with strong governance and transparency.
What we offer
- High-impact work with leading organisations across sectors, within a collaborative engineering-led AI capability.
- Continuous development through the Applied AI Engineering Academy, where you both advance your expertise in scalable AI system design and contribute to the evolution of engineering standards, reusable accelerators and capability development across the team.
- Opportunities to participate in innovation challenges, internal accelerators and capability showcases.
- 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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