AI Engineering Lead / Manager (Short-Term Consulting)

About the Role

GT is seeking an experienced AI Engineering Lead / Manager for a crucial short-term consulting engagement. You will focus on enhancing client engineering teams' AI-assisted software engineering maturity across people, processes, and technology. This role involves advising engineering teams, assessing current practices, recommending improvements, and contributing hands-on to AI engineering initiatives, including LLM applications, RAG pipelines, AI agents, and developer productivity tooling.

About the Client & Project

Our client, a leading global consulting firm, is undertaking an AI Engineering Excellence engagement for a US-based end client. The project's core objective is to boost engineering productivity and software delivery quality through AI-assisted development, LLM applications, RAG pipelines, AI agents, and modern software engineering best practices. This is a client-facing and hands-on role, requiring close collaboration with consulting stakeholders, engineering teams, product/design, and architecture/platform teams. The initial engagement is set for 6-8 weeks, with some overlap required for US working hours.

Responsibilities

• 80% Technical Guidance: Provide technical leadership 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 the adoption of AI tools (e.g., Claude Code, Cursor, Codex, GitHub Copilot).
• Define technical approaches for product architecture, data flows, integrations, and build processes.
• 20% Hands-on Architecture & Delivery: Engage in designing, developing, and documenting AI applications aligned with business outcomes.
• Build or support LLM-powered applications, RAG pipelines, and AI agent systems.
• Translate business requirements into technical solutions, contributing to implementation, testing, and code reviews.

Essential Knowledge, Skills & Experience

• Strong background in software engineering, full-stack development, backend engineering, or software architecture.
• Proficient hands-on Python experience.
• Experience with microservice API development (REST, GraphQL, or gRPC).
• Familiarity with API frameworks and tooling (FastAPI, Swagger, OpenAPI, or similar).
• Practical experience with AI-assisted software development tools (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 optimize retrieval-augmented generation (RAG) pipelines.
• Understanding of tokenization, context window limits, hallucination risks, model performance, and cost optimization.
• Strong knowledge of software engineering best practices (automated testing, CI/CD, clean code, documentation, code review).
• Solid computer science fundamentals (data structures, algorithms, automated testing, OOP, performance complexity).
• Ability to translate business requirements into clear technical requirements and implementation plans.
• Excellent communication skills for explaining technical concepts to diverse audiences.
• 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 (AWS, GCP, or Azure).
• Experience with SQL or NoSQL databases (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

1. GT interview with Recruiter
2. Technical interview
3. Final interview