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