AI Engineering Lead / Manager | NDA

GT was founded in 2019 by a former Apple, Nest, and Google executive. GT's mission is to connect the world's best talent with product careers offered by high-growth companies in the UK, USA, Canada, Germany, and the Netherlands. On behalf of our client, GT is looking for an AI Engineering Lead / Manager interested in a short-term consulting engagement focused on AI-assisted software engineering, developer productivity, LLM applications, and modern engineering transformation for a US-based end client. About the Client & the Project Our client is a leading global consulting firm delivering an AI Engineering Excellence engagement for a US-based end client. The project focuses on improving engineering productivity and software delivery quality through AI-assisted development practices, LLM applications, RAG pipelines, AI agents, and modern software engineering best practices. The role is client-facing and hands-on, working with consulting stakeholders, engineering teams, product/design, and architecture/platform teams. • Setup: initial 6–8 week engagement, some US-hours overlap required About the Role The role is focused on helping client engineering teams improve their AI-assisted engineering maturity across people, process, and technology. The consultant will advise engineering teams, assess current software development practices, recommend improvements, and contribute to hands-on AI engineering work, including LLM applications, RAG pipelines, AI agents, and developer productivity tooling. Responsibilities: • Spend around 80% of the role providing technical guidance to client and consulting teams on AI-assisted software engineering, developer productivity, architecture, microservices, build processes, CI/CD, testing, security, and engineering workflows. • Advise and coach engineering teams on modern software engineering practices and adoption of AI tools such as Claude Code, Cursor, Codex, or GitHub Copilot. • Define technical approaches for product architecture, data flows, integrations, and build processes. • Spend around 20% of the role on hands-on architecture and delivery, including designing, developing, and documenting AI applications aligned to business outcomes. • Build or support LLM-powered applications, RAG pipelines, and AI agent systems. • Translate business requirements into technical solutions and contribute to implementation, testing, and code reviews. Essential knowledge, skills & experience: • Strong background in software engineering, full-stack development, backend engineering, or software architecture. • Strong hands-on Python experience. • Experience with microservice API development, such as REST, GraphQL, or gRPC. • Experience with API frameworks and tooling such as FastAPI, Swagger, OpenAPI, or similar. • Practical experience with AI-assisted software development tools such as Claude Code, Cursor, Codex, GitHub Copilot, or similar. • Hands-on experience with LLM applications, prompt engineering, structured prompting, RAG, AI agents, or model routing. • Deep understanding of large language models and transformer architectures. • Ability to design, build, and optimise retrieval-augmented generation pipelines. • Understanding of tokenisation, context window limits, hallucination risks, model performance, and cost optimisation. • Strong knowledge of software engineering best practices, including automated testing, CI/CD, clean code, documentation, and code review. • Strong computer science fundamentals, including data structures, algorithms, automated testing, object-oriented programming, and performance complexity. • Ability to translate business requirements into clear technical requirements and implementation plans. • Strong communication skills and ability to explain technical concepts to both technical and non-technical stakeholders. • Comfortable working in a client-facing environment. • Ability to work with some overlap with US working hours. Nice-to-have • Deep embedded development and/or telco hardware experience. • Experience in hardware-adjacent, telecom, network equipment, embedded systems, or firmware environments. • Previous consulting, advisory, or enterprise client-facing delivery experience. • Experience working with Fortune 500 / Global 1000 clients. • Experience with public cloud platforms such as AWS, GCP, or Azure. • Experience with SQL or NoSQL databases such as PostgreSQL, MongoDB, or SQL Server. • Experience in engineering productivity, developer experience, internal developer platforms, or platform engineering. • Master's degree in Computer Science or a related technical field. Interview Steps • GT interview with Recruiter • Technical interview • Final interview