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.
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
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.
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.
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.
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.
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