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- Tasks: Lead a squad to build and scale AI-enabled solutions, ensuring reliability and security.
- Company: Join a forward-thinking organisation at the forefront of AI engineering.
- Benefits: Competitive pay, flexible working, continuous learning, and innovation opportunities.
- Other info: Engage in exciting projects and enhance your skills in a dynamic environment.
- Why this job: Shape the future of AI systems while mentoring and leading a talented team.
- Qualifications: Experience in engineering leadership and product strategy, with a focus on AI.
The predicted salary is between 80000 - 100000 ÂŁ per year.
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.
Client‑facing engineering includes Kubernetes/Docker; serverless; IAM, VNETs, zero‑trust patterns and secure network architecture. Data engineering architecture involves Spark/Databricks, ETL/ELT frameworks; big‑data/graph stacks (Hadoop, Cassandra, Neo4j); streaming (Event Hub/Kafka). Enterprise integration covers legacy/LOB systems, event workflows, case management platforms; design for high availability, resilience and observability.
Product leadership entails conducting discovery, framing hypotheses, shaping MVPs, backlog ownership, value/adoption metrics and client‑ready PRDs. Responsible AI requires a strong awareness of the UK financial‑services regulatory context (FCA, PRA, GDPR). Consulting leadership includes 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.
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: 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.
Applied AI Engineering Squad Lead - Product & Engineering, Senior Manager, TC employer: Ernst & Young
Contact Detail:
Ernst & Young Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Applied AI Engineering Squad Lead - Product & Engineering, Senior Manager, TC
✨Tip Number 1
Network like a pro! Get out there and connect with folks in the AI and engineering space. Attend meetups, webinars, or even just grab a coffee with someone in the industry. You never know who might have the inside scoop on job openings or can put in a good word for you.
✨Tip Number 2
Show off your skills! Create a portfolio that highlights your projects, especially those related to AI engineering. Whether it's GitHub repos or case studies, having tangible evidence of your work can really set you apart when you're chatting with potential employers.
✨Tip Number 3
Prepare for interviews by diving deep into the company’s recent projects and challenges. Tailor your responses to show how your experience aligns with their needs, especially around responsible AI and scalable solutions. This shows you’re not just interested in any job, but specifically in what they do.
✨Tip Number 4
Don’t forget to apply through our website! We’ve got loads of opportunities waiting for talented individuals like you. Plus, it’s a great way to ensure your application gets seen by the right people. Let’s get you on board!
We think you need these skills to ace Applied AI Engineering Squad Lead - Product & Engineering, Senior Manager, TC
Some tips for your application 🫡
Tailor Your Application: Make sure to customise your CV and cover letter to highlight your experience in AI engineering and leadership. We want to see how your skills align with the role, so don’t hold back on showcasing relevant projects or achievements!
Showcase Your Technical Skills: Since this role is all about applied AI engineering, be sure to include specific technologies and methodologies you’ve worked with. Mention your experience with Kubernetes, Docker, or any relevant data engineering frameworks to catch our eye!
Demonstrate Leadership Experience: As a squad lead, we’re looking for someone who can guide teams effectively. Share examples of how you've led multi-disciplinary squads or managed complex delivery cycles. This will help us see your potential in shaping our technical direction.
Apply Through Our Website: We encourage you to submit your application through our website. It’s the best way for us to receive your details and ensures you’re considered for the role. Plus, it’s super easy to do!
How to prepare for a job interview at Ernst & Young
✨Know Your AI Stuff
Make sure you brush up on the latest trends in applied AI engineering. Understand the key technologies mentioned in the job description, like Kubernetes, Docker, and data engineering frameworks. Being able to discuss these topics confidently will show that you're not just familiar with the buzzwords but can actually apply them.
✨Showcase Your Leadership Skills
As a squad lead, you'll need to demonstrate your ability to guide teams through complex delivery cycles. Prepare examples from your past experiences where you've successfully led multi-disciplinary squads, managed stakeholders, or navigated ambiguity. This will help the interviewers see you as a capable leader.
✨Understand Responsible AI
Familiarise yourself with responsible AI practices and the regulatory context in the UK, such as FCA and GDPR. Be ready to discuss how you would ensure compliance and ethical considerations in AI projects. This knowledge will set you apart as someone who takes these issues seriously.
✨Prepare for Technical Questions
Expect technical questions that assess your problem-solving skills and understanding of AI system architecture. Practice explaining complex concepts in simple terms, as this will be crucial when communicating with stakeholders. Use real-world scenarios to illustrate your thought process and decision-making.