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For Companies At a Glance
- Tasks: Lead the design and implementation of innovative AI solutions that drive business impact.
- Company: Join a forward-thinking tech company in Central London with a hybrid work culture.
- Benefits: Enjoy a competitive salary, flexible working options, and opportunities for professional growth.
- Other info: Collaborate with top-tier professionals and elevate engineering standards in a dynamic environment.
- Why this job: Be at the forefront of AI technology and influence strategic decisions across the organisation.
- Qualifications: 7+ years in software engineering with a focus on Generative AI and strong leadership skills.
The predicted salary is between 80000 - 100000 £ per year.
Key Responsibilities
- Strategic & Architectural Leadership
- Define and own the technical vision and architecture for AI solutions across the organization.
- Evaluate, select, and standardize AI technologies, frameworks, and third-party services.
- Lead technical design reviews and make critical architectural decisions for complex AI initiatives.
- Drive technical strategy for responsible AI, model governance, and production ML operations.
- Partner with senior leadership (CTO, VPs, Directors) to translate business objectives into technical AI roadmaps.
- Influence product and engineering strategy through technical insights and feasibility assessments.
- Technical Expertise & Execution
- Act as the go-to technical expert for complex AI challenges across engineering teams.
- Lead proof-of-concepts for emerging AI technologies and assess their production viability.
- Build and deliver production-ready AI and Generative AI solutions using LLMs, RAG architectures, agents, and responsible AI practices.
- Implement and maintain retrieval pipelines using embeddings, vector databases, hybrid search methods, and effective chunking strategies.
- Use AI coding assistants such as Cursor, GitHub Copilot, and Claude Code to accelerate development while maintaining ownership of outcomes and documenting best practices.
- Standards & Enablement
- Establish and enforce engineering best practices, coding standards, and quality benchmarks for AI development.
- Improve internal AI development tooling, including shared libraries, SDKs, and reference implementations for RAG, tracing, prompt management, and evaluation.
- Mentor engineers across all levels, conduct code reviews, and elevate engineering standards across the organization.
- Lead internal enablement and capability-building activities across the organization.
- Cross-functional Collaboration
- Collaborate closely with Product using a working-backwards approach, producing technical designs, breaking down work, and delivering iteratively.
- Partner with Security, Legal, and Data teams to define AI policies, review risks, and ensure privacy, PII protection, and regulatory compliance.
Skills, Knowledge and Expertise
- Must Have:
- 7+ years of software engineering experience with 3+ years focused on production Generative AI and RAG systems.
- Demonstrated experience architecting and scaling complex AI systems in production environments.
- Proven track record of technical decision-making and architectural leadership with measurable business impact.
- Deep technical expertise in LLMs, RAG, agentic workflows, prompt engineering, embeddings, vector databases, and hybrid search techniques.
- Hands-on experience with leading LLM providers (Anthropic Claude, OpenAI), including model selection, evaluation, and optimization.
- Expert-level Python development skills and fluency with AI coding assistants (Cursor, GitHub Copilot, Claude).
- Production experience with AWS cloud services and container orchestration (Kubernetes), including infrastructure design for ML workloads.
- Strong technical communication skills with ability to influence senior stakeholders and drive consensus across teams.
- Strong data engineering capabilities, including dataset creation, ETL development, and metrics definition.
- Solid understanding of ML fundamentals, experimentation methodologies, and model performance optimization.
- Nice to Have:
- Experience with model fine-tuning, RLHF, or custom training approaches.
- Familiarity with MLOps platforms and experiment tracking tools.
- Experience with infrastructure as code (Terraform, CloudFormation).
- Background in NLP research or open-source AI/ML contributions.
Lead AI Engineer in London employer: Robson Bale
Contact Detail:
Robson Bale Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Lead AI Engineer in London
✨Tip Number 1
Network like a pro! Reach out to your connections in the AI field, attend meetups, and engage in online forums. The more people you know, the better your chances of landing that Lead AI Engineer role.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your AI projects, especially those involving LLMs and RAG systems. This will give potential employers a taste of what you can do and set you apart from the competition.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and soft skills. Be ready to discuss your architectural decisions and how you've influenced product strategy in past roles. Confidence is key!
✨Tip Number 4
Don't forget to apply through our website! We love seeing candidates who are genuinely interested in joining our team. Plus, it gives you a chance to showcase your enthusiasm for the role right from the start.
We think you need these skills to ace Lead AI Engineer in London
Some tips for your application 🫡
Show Off Your Technical Vision: When you’re writing your application, make sure to highlight your technical vision and architectural leadership. We want to see how you can define and own AI solutions that align with our goals. Don’t hold back on showcasing your experience with complex AI systems!
Be Specific About Your Expertise: We love details! In your application, be specific about your hands-on experience with LLMs, RAG systems, and any AI coding assistants you've used. This is your chance to show us how your skills can directly impact our projects.
Collaborate Like a Pro: Collaboration is key at StudySmarter. Make sure to mention any past experiences where you’ve partnered with cross-functional teams. We want to know how you’ve translated business objectives into technical roadmaps and influenced product strategy.
Apply Through Our Website: Don’t forget to apply through our website! It’s the best way for us to keep track of your application and ensure it gets the attention it deserves. Plus, it shows you’re serious about joining our team!
How to prepare for a job interview at Robson Bale
✨Know Your AI Stuff
Make sure you brush up on your knowledge of LLMs, RAG systems, and the latest AI technologies. Be ready to discuss your hands-on experience with these tools and how you've applied them in real-world scenarios. This is your chance to showcase your technical expertise!
✨Showcase Your Leadership Skills
As a Lead AI Engineer, you'll need to demonstrate your ability to lead architectural decisions and mentor others. Prepare examples of how you've influenced technical strategy or improved engineering standards in previous roles. Highlight your experience in driving consensus among teams.
✨Prepare for Technical Challenges
Expect to face complex AI challenges during the interview. Think about potential proof-of-concepts you've led and be ready to discuss how you approached these challenges. Practise explaining your thought process clearly, as strong communication is key to influencing senior stakeholders.
✨Understand the Business Side
It's not just about the tech; you need to connect AI solutions to business objectives. Familiarise yourself with the company's goals and think about how your technical vision can align with their strategic roadmap. Be prepared to discuss how you've translated business needs into technical solutions in the past.