AI Engineering Lead / Manager | NDA
GT · Europe, Poland
Job Description
AI Engineering Lead / Manager (Contract, Remote)
GT is seeking an experienced AI Engineering Lead / Manager for a short-term consulting engagement. This remote role will focus on AI-assisted software engineering, developer productivity, and modern engineering transformation for a US-based client.
About the Client & Project
Our client is a global consulting firm engaged in an AI Engineering Excellence initiative. The project aims to enhance engineering productivity and software delivery quality for a US-based end client by leveraging AI-assisted development, LLM applications, RAG pipelines, AI agents, and modern software engineering practices. This is a client-facing, hands-on role requiring 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 with US working hours.
About the Role
This position involves guiding client engineering teams to improve their AI-assisted engineering maturity across people, process, and technology. You will assess current software development practices, recommend improvements, and contribute directly to AI engineering tasks, including LLM applications, RAG pipelines, AI agents, and developer productivity tooling.
Key Responsibilities
- Technical Guidance (80%):
- 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.
- Hands-on Architecture & Delivery (20%):
- Design, develop, and document 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.
Requirements
- Strong background in software engineering, full-stack development, backend engineering, or software architecture.
- Proficient hands-on Python experience.
- Experience with microservice API development (e.g., REST, GraphQL, gRPC).
- Familiarity with API frameworks and tooling (e.g., FastAPI, Swagger, OpenAPI).
- Practical experience with AI-assisted software development tools (e.g., 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 optimize retrieval-augmented generation (RAG) pipelines.
- Understanding of tokenisation, context window limits, hallucination risks, model performance, and cost optimisation.
- 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, with the ability to explain technical concepts to diverse stakeholders.
- Comfortable in a client-facing environment.
- Ability to work with some overlap with US working hours.
Nice-to-Have Skills
- 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, Azure).
- Experience with SQL or NoSQL databases (e.g., PostgreSQL, MongoDB, 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 Process
- GT Interview with Recruiter
- Technical Interview
- Final Interview
✨ This description was enhanced by AI based on the original listing.