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For Companies At a Glance
- Tasks: Lead a squad to build and scale AI-enabled solutions that make a real impact.
- Company: Join a leading tech firm focused on innovative AI engineering.
- Benefits: Enjoy competitive pay, flexible working, and continuous learning opportunities.
- Other info: Be part of a culture that values inclusion and personal growth.
- Why this job: Shape the future of AI while collaborating with top talent in a dynamic environment.
- Qualifications: Expertise in software engineering and applied AI/ML is essential.
The predicted salary is between 80000 - 100000 ÂŁ per year.
Position Details
- Location: UK (London / Manchester / Birmingham / Edinburgh) — Hybrid working with client‑site travel as required.
- Contract: Permanent, full‑time
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 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.
Applied AI Engineering Squad Lead - Product & Engineering, Senior Manager, TC in Edinburgh employer: EY
Contact Detail:
EY Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Applied AI Engineering Squad Lead - Product & Engineering, Senior Manager, TC in Edinburgh
✨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!
✨Tip Number 2
Show off your skills! Create a portfolio or GitHub repository showcasing your projects, especially those related to AI and engineering. This gives potential employers a taste of what you can do and sets you apart from the crowd.
✨Tip Number 3
Prepare for interviews by practising common questions and scenarios specific to applied AI engineering. Think about how you would lead a squad or tackle a complex problem. The more you rehearse, the more confident you'll feel when it’s showtime!
✨Tip Number 4
Don’t forget to apply through our website! We love seeing candidates who are genuinely interested in joining us at StudySmarter. Tailor your application to highlight your experience in AI and engineering, and let’s get the conversation started!
We think you need these skills to ace Applied AI Engineering Squad Lead - Product & Engineering, Senior Manager, TC in Edinburgh
Some tips for your application 🫡
Tailor Your Application: Make sure to customise your CV and cover letter to highlight your experience with AI engineering and leadership. We want to see how your skills align with the role, so don’t hold back on showcasing your relevant projects!
Showcase Your Technical Skills: We’re looking for expertise in Python, cloud architecture, and AI/ML mastery. Be specific about your technical abilities and any relevant certifications you have. This is your chance to shine, so let us know what you can bring to the table!
Demonstrate Your Leadership Experience: As a squad lead, you’ll need to guide teams effectively. Share examples of how you’ve led multi-disciplinary squads or managed complex delivery cycles. We love seeing how you’ve made an impact in previous roles!
Apply Through Our Website: Don’t forget to submit your application through our website! It’s the best way for us to keep track of your application and ensure it gets the attention it deserves. We can’t wait to hear from you!
How to prepare for a job interview at EY
✨Know Your Tech Inside Out
Make sure you’re well-versed in the essential skills listed in the job description, like Python, distributed systems, and cloud architecture. Brush up on your knowledge of AI/ML concepts, especially those related to NLP and reinforcement learning, as these will likely come up during technical discussions.
✨Showcase Your Leadership Skills
As a squad lead, you'll need to demonstrate your ability to guide teams effectively. Prepare examples of how you've led multi-disciplinary squads through complex projects, focusing on your approach to stakeholder management and delivery transparency.
✨Prepare for Client Engagement Scenarios
Since this role involves client-facing responsibilities, think about how you would shape an AI solution vision with senior stakeholders. Be ready to discuss past experiences where you’ve successfully partnered with clients to drive value realisation and adoption strategies.
✨Emphasise Continuous Improvement
Highlight your commitment to mentoring and developing talent within your team. Share specific instances where you’ve contributed to thought leadership or improved engineering practices, as this aligns with the role's focus on shaping product thinking and fostering a culture of continuous improvement.