AI Engineering Lead Manager | NDA
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Job Summary
GT was founded in 2019 by a former Apple Nest and Google executive.GTs mission is to connect the worlds 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 68 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.
Masters degree in Computer Science or a related technical field.
Interview Steps
GT interview with Recruiter
Technical interview
Final interview
Required Experience:
Manager
About Company
GT provides high-growth product companies around the world with offshore product teams from Eastern Europe, an end-to-end product development studio, software development, and data science services.
