At a Glance
- Tasks: Lead a squad in delivering innovative AI solutions and mentor talented engineers.
- Company: Join a forward-thinking tech company focused on applied AI and engineering excellence.
- Benefits: Enjoy competitive pay, flexible hybrid work, and continuous learning opportunities.
- Other info: Be part of an inclusive culture that values diversity and growth.
- Why this job: Shape the future of AI while making a real impact in a dynamic environment.
- Qualifications: Expertise in software engineering and deep knowledge of applied AI/ML required.
The predicted salary is between 70000 - 90000 € per year.
Location: UK (London / Manchester / Birmingham / Edinburgh) — Hybrid working with client-site travel as required. Contract: Permanent, full-time.
Responsibilities
- Define and drive the strategic direction for the Applied AI Engineering squad, including roadmap priorities, solution scope, and delivery outcomes.
- Partner with senior client stakeholders to shape AI solution vision, adoption strategies, and value realisation.
- Lead the end‑to‑end delivery of AI‑enabled systems, including agents, retrieval systems, and supporting services, ensuring alignment with enterprise architecture, responsible‑AI requirements, and operational readiness.
- Establish strong engineering practices and ways‑of‑working across the squad, including review practices, reliability patterns, and observability.
- Shape product thinking around applied AI solutions, translating opportunities into scalable designs and guiding the team in MVP development.
- Mentor engineers, develop high‑potential talent, and contribute to thought leadership and market‑facing initiatives.
- Manage dependencies, risks, and delivery transparency across complex programmes.
Qualifications – Essential Skills & Experience
- Expertise in software and systems engineering (Python/TypeScript, distributed systems, API/microservice architecture, cloud‑native patterns).
- Deep applied AI/ML mastery (NLP/CV/transformers, generative models, reinforcement learning, classical ML, statistical modelling).
- Advanced LLM/RAG engineering (prompt pipelines, embeddings, vector stores, hybrid retrieval, hallucination mitigation, evaluation frameworks).
- LLMOps/MLOps experience (automated testing, drift monitoring, safety guardrails, CI/CD for ML, telemetry, lineage, governance).
- Cloud architecture leadership (Azure preferred, AWS/GCP; Kubernetes/Docker, serverless, IAM, VNETs, zero‑trust, secure network architecture).
- Data engineering architecture (Spark/Databricks, ETL/ELT frameworks, big‑data/graph stacks, streaming).
- Enterprise integration (legacy/LOB systems, event workflows, case management platforms, high‑availability, resilience, observability).
- Product leadership (discovery, hypotheses, MVPs, backlog ownership, value/adoption metrics, PRDs).
- Responsible AI & compliance (privacy‑by‑design, auditability, fairness, transparency, UK financial‑services regulatory context).
- Consulting leadership (stakeholder management, commercial awareness, proposal shaping, solution positioning, thought leadership).
- Demonstrated ability to lead multi‑disciplinary squads through complex delivery cycles.
Nice to Have
- Governance/model‑risk/responsible‑AI certifications.
Preferred Technical Certifications
- AWS, Azure, or Google ML and architecture certifications (AI‑102, Azure Data Scientist Associate, AWS Machine Learning Specialty, Google Professional ML Engineer, Databricks ML Engineer, Kubernetes CKA/CKAD, Azure/AWS Solutions Architect).
Working Style
- Hands‑on when needed, but primarily create the conditions for repeatable delivery: clear direction, strong ways‑of‑working, high engineering standards.
- Earn trust with senior stakeholders by explaining trade‑offs simply and steering delivery through ambiguity with governance and transparency.
Benefits & Development
- Continuous development through the Applied AI Engineering Academy.
- 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
We are committed to building an inclusive culture where everyone can thrive. If you require adjustments or support during the recruitment process, please let us know.
Applied AI Engineering Squad Lead - Product & Engineering, Senior Manager, , TC FS in Birmingham employer: EY
As an employer, we pride ourselves on fostering a dynamic and inclusive work culture that prioritises continuous development and innovation. With access to the Applied AI Engineering Academy and opportunities for hands-on leadership in cutting-edge AI projects, our team members thrive in a supportive environment that encourages professional growth and collaboration. Our flexible hybrid working model across major UK cities ensures a balanced work-life integration while engaging with diverse client challenges.
StudySmarter Expert Advice🤫
We think this is how you could land Applied AI Engineering Squad Lead - Product & Engineering, Senior Manager, , TC FS in Birmingham
✨Tip Number 1
Network like a pro! Reach out to folks in your industry on LinkedIn or at events. A friendly chat can open doors that a CV just can't.
✨Tip Number 2
Prepare for those interviews by practising common questions and scenarios related to AI engineering. We want you to showcase your expertise and confidence!
✨Tip Number 3
Don’t forget to follow up after interviews! A quick thank-you email can leave a lasting impression and show your enthusiasm for the role.
✨Tip Number 4
Apply through our website for the best chance of landing that dream job. We love seeing candidates who are proactive and engaged with our company!
We think you need these skills to ace Applied AI Engineering Squad Lead - Product & Engineering, Senior Manager, , TC FS in Birmingham
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 responsibilities outlined in the job description.
Showcase Your Technical Skills:Don’t hold back on showcasing your expertise in software engineering, cloud architecture, and applied AI/ML. Use specific examples from your past work to demonstrate your capabilities and how they relate to the role.
Be Clear and Concise:When writing your application, keep it clear and to the point. We appreciate straightforward communication, so avoid jargon unless it's relevant to the role. Make it easy for us to see why you’re a great fit!
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way to ensure your application gets into the right hands and shows us you’re serious about joining our team!
How to prepare for a job interview at EY
✨Know Your AI Stuff
Make sure you brush up on your applied AI and machine learning knowledge. Be ready to discuss specific technologies like NLP, CV, and LLMOps. The interviewers will want to see that you can not only talk the talk but also walk the walk when it comes to AI solutions.
✨Showcase Your Leadership Skills
As a squad lead, you'll need to demonstrate your ability to manage teams and projects. Prepare examples of how you've led multi-disciplinary squads through complex delivery cycles. Highlight your mentoring experience and how you've developed talent in previous roles.
✨Understand the Business Context
Familiarise yourself with the financial services sector and the regulatory context in the UK. Be prepared to discuss how responsible AI practices can be integrated into business strategies. This shows that you understand the bigger picture and can align AI solutions with business goals.
✨Prepare for Technical Questions
Expect deep dives into your technical expertise, especially around software engineering and cloud architecture. Brush up on your knowledge of Python, TypeScript, and cloud platforms like Azure. Be ready to discuss architectural patterns and how you've implemented them in past projects.
