Lead AI Engineer - Permanent - London/Hybrid

Job Description

Permanent Hybrid in Central London

Competitive Salary

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.