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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