At a Glance
- Tasks: Lead AI engineering projects, mentor teams, and ensure systems are production-ready.
- Company: Dynamic tech firm focused on innovative AI solutions.
- Benefits: Hybrid work model, competitive salary, and opportunities for professional growth.
- Other info: Join a collaborative environment with exciting career advancement opportunities.
- Why this job: Shape the future of AI while working with cutting-edge technologies.
- Qualifications: Expertise in software engineering, applied AI, and cloud architecture.
The predicted salary is between 80000 - 100000 £ per year.
Location: London, Manchester, Birmingham, Edinburgh, Belfast
Hybrid with client-site travel
Contract: Permanent, full-time
Role purpose: Lead technical delivery across solution threads: set technical direction, mentor engineers, and ensure systems are production-ready (reliability, observability, security, runbooks). Continue your development through the Applied AI Engineering Academy focused on advanced patterns and engineering leadership.
What you’ll do:
- Client-facing engineering & leadership: Shape engineering approaches; engage senior stakeholders; articulate trade-offs; ensure engineering quality across squads and complex client environments.
- Solution architecture & implementation leadership: Architect enterprise-grade AI services (agents, RAG pipelines, orchestration layers, platform components); ensure operational readiness; drive Responsible AI, evaluation and best practices.
- Product mindset & continuous improvement: Mentor engineers; lead technical reviews; establish reference architectures and reusable accelerators; contribute to internal knowledge sharing and external thought leadership.
What we’re looking for:
- Essential: Deep software/systems engineering (Python/TypeScript, distributed systems, CI/CD). Applied-AI expertise: LLM/RAG engineering; evaluation; telemetry/drift monitoring; versioning and release management. Cloud architecture (Azure/AWS/GCP), Kubernetes/Docker, serverless, IAM and network security. Data engineering depth (Spark/Databricks; ETL/ELT); cloud-native data + AI architectures. Enterprise integration and SRE principles (SLIs/SLOs, runbooks, rollback). Consulting leadership: stakeholder, budget and risk management; team leadership.
- Nice to have: Graph/big-data stacks; streaming; cloud architect certifications and Responsible AI governance credentials.
Travel & working model: Hybrid with periodic client travel across the UK (and occasional international travel).
Additional educational preference: A PhD in Computer Science, Applied Mathematics, or Computer Engineering is desirable but not essential.
Senior/Lead Applied AI Engineer in London employer: 慨正橡扯
As a Senior Applied AI Engineer at our company, you will thrive in a dynamic and innovative environment that champions technical excellence and continuous learning. With access to the Applied AI Engineering Academy, you'll have unparalleled opportunities for professional growth while working alongside industry leaders in a hybrid model that promotes work-life balance. Our collaborative culture encourages mentorship and knowledge sharing, making us an exceptional employer for those seeking to make a meaningful impact in the field of AI.
StudySmarter Expert Advice🤫
We think this is how you could land Senior/Lead Applied AI Engineer in London
✨Tip Number 1
Network like a pro! Get out there and connect with folks in the AI engineering space. Attend meetups, webinars, or industry events to meet potential employers and showcase your expertise.
✨Tip Number 2
Show off your skills! Create a portfolio that highlights your projects and achievements in applied AI. This will give you an edge when chatting with hiring managers and help them see what you can bring to the table.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and soft skills. Be ready to discuss your experience with Python, cloud architecture, and mentoring engineers, as these are key areas for the role.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets noticed. Plus, we love seeing candidates who are proactive about their job search.
We think you need these skills to ace Senior/Lead Applied AI Engineer in London
Some tips for your application 🫡
Tailor Your CV:Make sure your CV reflects the skills and experiences that match the Senior Applied AI Engineer role. Highlight your expertise in Python, cloud architecture, and any relevant projects you've led. We want to see how you can bring value to our team!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you're passionate about applied AI and how your background aligns with our mission at StudySmarter. Don’t forget to mention your leadership experience and client-facing skills!
Showcase Your Projects:If you've worked on any cool AI projects or have contributions to open-source, make sure to include them. We love seeing practical examples of your work, especially those that demonstrate your problem-solving skills and technical leadership.
Apply Through Our Website:We encourage you to apply directly through our website for the best chance of getting noticed. It’s super easy, and you’ll be able to keep track of your application status. Plus, we love seeing candidates who take the initiative!
How to prepare for a job interview at 慨正橡扯
✨Know Your Tech Inside Out
Make sure you’re well-versed in the technologies mentioned in the job description, like Python, TypeScript, and cloud platforms. Brush up on your knowledge of distributed systems and CI/CD practices, as these will likely come up during technical discussions.
✨Showcase Your Leadership Skills
Since this role involves mentoring and leading teams, be prepared to share examples of how you've successfully guided engineers in the past. Highlight your experience in stakeholder engagement and how you've managed budgets and risks in previous projects.
✨Prepare for Client-Facing Scenarios
As the position is client-facing, think about how you would articulate complex technical concepts to non-technical stakeholders. Practice explaining trade-offs in engineering decisions and how you ensure quality across different squads.
✨Emphasise Continuous Improvement
Demonstrate your commitment to continuous learning and improvement. Discuss any initiatives you've led that focused on best practices in AI engineering or how you've contributed to internal knowledge sharing. This shows you're not just a doer but also a thinker who values growth.
