Principle AI Engineer - Permanent - London/Hybrid - MUST have an active GitHub Account

Principle AI Engineer - Permanent - London/Hybrid

• Permanent
• Hybrid in Central London
• Competitive Salary

Candidates MUST have an active GitHub account to be considered for this role

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 (upgraded from "mentor peers")
• Lead internal enablement and capability-building activities across the organization (upgraded from "contribute to")

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 (moved from Nice to Have)
• Solid understanding of ML fundamentals, experimentation methodologies, and model performance optimization (moved from Nice to Have)

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