At a Glance
- Tasks: Lead a squad to build and scale AI-enabled solutions that make a real impact.
- Company: Join EY, a leader in innovative AI engineering with a collaborative culture.
- Benefits: Enjoy competitive pay, flexible hybrid work, and continuous learning opportunities.
- Other info: Be part of a dynamic team with excellent career growth and mentorship opportunities.
- Why this job: Shape the future of AI while working on exciting projects with top organisations.
- Qualifications: Expertise in software engineering and applied AI/ML is essential.
The predicted salary is between 70000 - 90000 £ per year.
Location: London
Salary: Competitive
Date: 8 May 2026
Requisition ID:
Contract: Permanent, full-time
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 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. You will benefit from:
- 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.
Applied AI Engineering Lead in London employer: 慨正橡扯
As an Applied AI Engineering Lead at EY, you will join a dynamic and innovative team in London, where collaboration and continuous development are at the forefront of our work culture. We offer competitive compensation, flexible hybrid working arrangements, and access to the Applied AI Engineering Academy, ensuring that you not only advance your technical expertise but also contribute to shaping the future of AI solutions. With a strong focus on responsible AI practices and opportunities for mentorship, EY is committed to fostering an inclusive environment that supports your professional growth and success.
StudySmarter Expert Advice🤫
We think this is how you could land Applied AI Engineering Lead in London
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We think you need these skills to ace Applied AI Engineering Lead in London
Some tips for your application 🫡
Show off your coding skills:When applying for a software engineering role, it's super important to showcase your coding skills. Make sure your CV includes your tech stack, any relevant programming languages you’re comfortable with, and examples of projects you've worked on. If you have a GitHub profile, link it up! We love to see code in action.
Tailor your portfolio:For a full-time role, we’d expect to see some solid examples of your work in your portfolio. Make sure to include at least two or three projects that highlight your problem-solving skills and your ability to work with different technologies. Focus on the projects that are most relevant to the position at 慨正橡扯.
Craft a killer cover letter:Your cover letter is your chance to stand out—make it personal! Explain why you want to work at 慨正橡扯 and how your skills align with the role. Show us your passion for software development. We dig enthusiastic candidates who understand the value of collaboration and continuous learning!
Be clear and concise:When it comes to writing your CV and cover letter, clarity is key. Avoid jargon that could confuse us and stick to simple, direct language. Highlight your achievements with quantifiable results where possible, and keep everything easy to read. A well-organised application goes a long way!
How to prepare for a job interview at 慨正橡扯
✨Brush Up on Your Coding Skills
For a full-time software engineering role, it's crucial that we stay sharp with our coding abilities. Expect technical questions that might involve solving problems on the spot or discussing algorithms. Practise on platforms like LeetCode or HackerRank to get comfortable with the types of questions that often come up.
✨Know Your Tools and Frameworks
Make sure we’re well-acquainted with the tools and technologies listed in the job description. Familiarise ourselves with any specific frameworks or programming languages mentioned. If 慨正橡扯 uses React or Node.js, for instance, be ready to discuss how we’ve used them in previous projects or coursework.
✨Showcase Your Projects
Bring along a portfolio that highlights our best work. This could be code samples, GitHub repositories, or any side projects we’ve built. Make sure we can talk through our thought process for each project, especially the challenges we faced and how we solved them—this shows our problem-solving skills in action.
✨Prepare for Behavioural Questions
While technical skills are key, full-time positions also require cultural fit. Be ready to discuss our previous experiences and how we handle teamwork, conflict, and deadlines. Brush up on the STAR method—Situation, Task, Action, Result—to clearly articulate our past experiences when discussing how we've contributed to a team.
