Senior AI Engineer

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.
• Diagnose 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.