Senior AI Engineer - Permanent - London/Hybrid

Permanent
Hybrid in Central London
Competitive Salary

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.

Engineering Standards & Enablement

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

Must Have

5+ years of software engineering experience, including 2+ years 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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