Applied AI Engineering Squad Lead - Product & Engineering, Senior Manager, , TC FS

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Job Title: Applied AI Engineering Squad Lead — Product & Engineering

EY Grade: Senior Manager (UK)

Location: UK (London / Manchester / Birmingham / Edinburgh) — Hybrid working with client-site travel as required.

Contract: Permanent, full-time

Why EY

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.

Applied AI Engineering focuses on the hands-on engineering required to build, test and support these systems—aligned to EY platform patterns, responsible‑AI guardrails, and governance.

As an Applied AI Engineering Squad Lead, you will act as a senior engineering and product leader, guiding squad teams in building and scaling AI-enabled solutions. You will shape the technical direction, product vision and delivery approach for applied AI systems across engagements, ensuring that solutions deliver measurable value while meeting enterprise standards for reliability, security and responsible AI.

You will lead a 4-7 person Applied AI Engineering squad, bringing together engineers, architects and designers to deliver AI systems. You will ensure technical coherence across delivery, establish strong engineering practices and help organisations successfully operationalise AI capabilities.

As part of the Applied AI Engineering Academy, you will both deepen and share advanced engineering capabilities across the team. The academy supports continued development in areas such as AI system architecture, scalable engineering patterns and responsible AI practices, while also providing a platform to mentor engineers, contribute reusable patterns and help shape the technical standards of the capability.

Through collaborative engineering challenges, knowledge sharing and capability initiatives, you will play an active role in strengthening how Applied AI Engineering solutions are designed, delivered and scaled across engagements.

In this lead role, you will operate at the intersection of engineering leadership, product strategy and client engagement, shaping how AI-enabled systems are designed, delivered and scaled in complex enterprise environments.

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 s