GT
Confidential
AIEngineeringLead/Manager
Neural analysis suggests this role is
optimal for Lead candidates.
“AI Engineering Lead / Manager at GT. Skills: AI Engineering, LLM applications, Developer productivity, Software architecture. Provide technical guidance. Advise engineering teams”
What You'll Achieve.
Improve engineering productivity; Improve software delivery quality; Align AI applications to business outcomes
Industry & Context.
Troubleshooting
US-hours overlap required
What They're Looking For.
Must Have
background in software engineering, full-stack development background, backend engineering background, software architecture background, hands-on Python experience, microservice API development experience, API frameworks and tooling experience, AI-assisted software development tools experience, LLM applications experience, prompt engineering experience, structured prompting experience, RAG experience, AI agents experience, model routing experience, Deep understanding of LLMs, Deep understanding of transformer architectures, Ability to design RAG pipelines, Ability to build RAG pipelines, Ability to optimise RAG pipelines, knowledge of software engineering best practices, computer science fundamentals knowledge, Ability to translate business requirements, communication skills, Ability to work with US hours
Nice to Have
Deep embedded development experience, Telco hardware experience, Hardware-adjacent experience, Telecom experience, Network equipment experience, Embedded systems experience, Firmware environments experience, Previous consulting experience, Advisory experience, Enterprise client-facing delivery experience, Experience with Fortune 500 clients, Experience with Global 1000 clients, Master’s degree in Computer Science
What You'll Do.
Provide technical guidance
Advise engineering teams
Coach engineering teams
Define technical approaches
Design AI applications
Develop AI applications
Document AI applications
Build LLM-powered applications
Support LLM-powered applications
Support RAG pipelines
Build AI agent systems
Support AI agent systems
Translate business requirements
Contribute to implementation
Contribute to testing
Contribute to code reviews
How You'll Work.
Team & Collaboration
Client teams; Consulting teams; Engineering teams; Product teams; Design teams; Architecture teams; Platform teams
Communication Scope
Explain technical concepts
Full Job Description
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
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