GT - AI Engineering Lead / Manager
Requirements
• Python • Experience with microservice API development, such as REST, GraphQL, or gRPC. • REST, GraphQL, or gRPC • Experience with API frameworks and tooling such as FastAPI, Swagger, OpenAPI, or similar. • FastAPI, Swagger, OpenAPI • Practical experience with AI-assisted software development tools such as Claude Code, Cursor, Codex, GitHub Copilot, or similar. • Claude Code, Cursor, Codex, GitHub Copilot • 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. • US working hours • Nice-to-have • 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. • 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 • Interview Steps • GT interview with Recruiter • Technical interview • Final interview
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.
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