Principle AI Engineer
3 days ago
London
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