Permanent, Hybrid in Central London
Key Responsibilities
- 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
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