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

Principle AI Engineer - Permanent - London/Hybrid

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

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