Senior AI Engineer

Permanent

Hybrid in Central London

Key Responsibilities

Technical Design & Delivery

• Contribute to the technical design and architecture of scalable AI solutions
• Evaluate AI technologies, frameworks, and third-party services, making recommendations based on technical and business requirements
• Participate in technical design reviews and support architectural decisions for complex AI initiatives
• Help implement responsible AI, model governance, and production machine learning practices
• Work with technical and product stakeholders to translate business requirements into practical AI solutions
• Provide technical insights and feasibility assessments to support product and engineering decisions

Technical Expertise & Execution

• Solve complex AI engineering challenges and provide technical guidance to other engineers
• Develop proof-of-concepts for emerging AI technologies and assess their suitability for production use
• 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
• Design evaluation approaches to assess model quality, retrieval performance, reliability, and business outcomes
• Use AI coding assistants such as Cursor, GitHub Copilot, and Claude Code to accelerate development while maintaining ownership of code quality and outcomes
• eDiagnose and resolve performance, scalability, reliability, and cost issues within production AI systems
• Contribute to engineering best practices, coding standards, and quality benchmarks for AI development
• Develop and improve internal AI tooling, including shared libraries, SDKs, and reusable components for RAG, tracing, prompt management, and evaluation
• Conduct code reviews and support the development of less-experienced engineers through mentoring and knowledge sharing
• Contribute to internal AI enablement activities, technical documentation, demonstrations, and best-practice guidance
• Promote maintainable, observable, secure, and well-tested approaches to AI engineering

Cross-functional Collaboration

• Collaborate closely with Product using a working-backwards approach, contributing to technical designs, breaking down work, and delivering iteratively
• Work with Security, Legal, and Data teams to apply AI policies and address privacy, PII protection, security, and regulatory requirements
• Communicate technical decisions, risks, trade-offs, and progress clearly to technical and non-technical stakeholders
• Partner with software, platform, and data engineers to integrate AI capabilities into wider products and services

Skills, Knowledge and Expertise

• Software engineering experience, including building production AI, Generative AI, or RAG systems
• Strong experience designing, building, deploying, and maintaining AI systems in production environments
• Demonstrated ability to make sound technical decisions and deliver solutions with measurable business impact
• Strong knowledge of LLMs, RAG, agentic workflows, prompt engineering, embeddings, vector databases, and hybrid search techniques
• Hands-on experience with leading LLM providers, such as Anthropic and OpenAI, including model selection, evaluation, and optimisation
• Advanced Python development skills and experience using AI coding assistants such as Cursor, GitHub Copilot, or Claude Code
• Production experience with AWS cloud services and containerised environments, including Kubernetes
• Experience building reliable APIs, services, and integration patterns for AI-enabled applications
• Strong data engineering capabilities, including dataset creation, ETL development, data quality management, and metrics definition
• Solid understanding of machine learning fundamentals, experimentation methodologies, and model performance optimisation
• Strong technical communication skills and the ability to collaborate effectively across engineering, product, data, security, and legal teams
• Experience applying software engineering practices such as automated testing, version control, continuous integration, observability, and documentation

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, such as Terraform or CloudFormation
• Experience with LLM evaluation, tracing, prompt management, or AI observability platforms
• Background in NLP research or contributions to open-source AI or machine learning projects

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