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