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AI Engineering Lead / Manager | NDA

Remote from
Europe flagPoland flag
Europe, Poland
Annual salary
Undisclosed
Salary information is not provided for this position. Check our Salary Directory to estimate the average compensation for similar roles.
Department
Software Engineering
Employment type
Full Time,
Job posted
Apply before
10 Jul 2026
Experience level
Senior
Views / Applies
17 / 4
About company Share

About GT

GT provides clients with offshore product teams from CEE, a product development studio & data science services.

Actively Hiring
Verified job posting
This job post has been manually reviewed for authenticity and compliance.

AI Summary

This is a short-term consulting role for an AI Engineering Lead/Manager to advise a US-based client on AI-assisted software engineering and developer productivity. The role involves 80% technical guidance and coaching, and 20% hands-on architecture and delivery of LLM applications, RAG pipelines, and AI agents. Requires strong Python, microservices, and AI tool expertise, with client-facing skills. The engagement is 6-8 weeks with US hours overlap.

Role DNA

Job Complexity
Easy Hard
Pace & Pressure
Relaxed Fast-paced
Autonomy Level
Guided Full Ownership
Communication Load
Independent Highly Collaborative
AI Insight The role demands deep expertise in AI engineering, LLMs, and consulting, plus hands-on skills and client management, making it quite challenging.

Salary Analysis

Median Market Rate
$190,000
US Market
$140k – 250k
0 $275k
AI Insight The offered salary is not specified, but for a senior AI Engineering Lead/Manager role in the US, the market range typically falls between $140,000 and $250,000 annually, with a median around $190,000. This consulting engagement likely offers competitive compensation commensurate with expertise.

Key Skills

AI Engineering LLM Applications RAG Pipelines Developer Productivity Python Microservices CI/CD Software Architecture Client-Facing Prompt Engineering

Dear Hiring Manager,

I am writing to express my strong interest in the AI Engineering Lead/Manager consulting role. With extensive experience in AI-assisted software engineering, LLM applications, and RAG pipelines, I am well-prepared to guide your client’s engineering teams toward improved productivity and delivery quality.

In my career, I have successfully led the adoption of AI development tools like Claude Code and GitHub Copilot, and have hands-on expertise in building robust microservices and CI/CD pipelines. I am comfortable balancing hands-on architecture with strategic coaching, and I excel at translating business needs into technical solutions.

This opportunity to drive AI engineering excellence for a leading global consulting firm aligns perfectly with my skills and passion. I look forward to the possibility of contributing to this impactful engagement.

Sincerely,
[Your Name]

Can you describe your experience with AI-assisted development tools like Claude Code, Cursor, or GitHub Copilot? How have they improved your engineering workflow?
I have used GitHub Copilot extensively for code completion and Claude Code for complex reasoning tasks. These tools reduced boilerplate coding by 30% and improved code quality by suggesting best practices. I've also trained teams to use them effectively, ensuring they augment rather than replace critical thinking.
Walk me through how you would assess and improve an engineering team's AI maturity. What metrics would you track?
I would start with a maturity model covering people, process, and technology. People: assess skills and adoption of AI tools. Process: evaluate CI/CD and testing automation. Technology: review codebase and tooling. Metrics include deployment frequency, lead time, change failure rate, and AI tool adoption rate. Then I'd create a roadmap for incremental improvements.
Explain a complex RAG pipeline you've built. What challenges did you face with retrieval quality or latency?
I built a RAG pipeline for a legal document search using Pinecone and an LLM. Challenges included chunking strategies and ensuring relevant context retrieval. We resolved this by implementing hybrid search (dense + sparse) and re-ranking. Latency was mitigated with caching and async processing.
How do you stay updated on the rapidly evolving AI engineering landscape? Can you give an example of a recent LLM advancement you applied?
I follow AI conferences (NeurIPS, O'Reilly AI), blogs, and GitHub repos. Recently, I used structured prompting (e.g., JSON mode) to improve consistency in model outputs. I also experimented with model routing to mix small and large models for cost efficiency.
This is a client-facing role. How would you communicate a technical concept like hallucination risk to a non-technical stakeholder?
I would explain hallucination as the model making up incorrect information, similar to a human confidently stating a false fact. I'd use an analogy like 'the model sometimes guesses when it doesn't know.' Then I would discuss mitigation strategies like grounding with retrieval and human review.

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 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.

  • Master’s degree in Computer Science or a related technical field.

Interview Steps

  1. GT interview with Recruiter

  2. Technical interview

  3. Final interview

Apply now >

Annual salary information is not provided for this position. Explore salary ranges for similar roles in our Salary Directory ›

This job listing has been manually reviewed by the Jobicy Trust & Safety Team for compliance with our posting guidelines, including verification of the company's legitimacy, accuracy of job details, clarity of remote work policy, and absence of misleading or fraudulent content.

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