Gen AI Adoption Lead (Engineering Productivity) (#5106)

N-iX is a global software development service company that helps businesses across the globe create next-generation software products. Founded in 2002, we unite 2,400+ tech-savvy professionals across 40+ countries, working on impactful projects for industry leaders and Fortune 500 companies. Our expertise spans cloud, data, AI/ML, embedded software, IoT, and more, driving digital transformation across finance, manufacturing, telecom, healthcare, and other industries. Join N-iX and become part of a team where your ideas make a real impact.

At N-iX, we work directly with client engineering teams to transform the way they build software with AI.

As an GenAI Adoption Lead, you will design practical agentic tooling, help engineering teams adopt it effectively, and demonstrate measurable improvements in productivity, quality, and delivery speed.

This role combines hands-on engineering with enablement, facilitation, and client-facing consulting.

What you will do

• Assess how client engineering teams work today and identify where their existing AI tools are being used inconsistently.
• Establish practical AI-enabled ways of working across different roles:
• Product Owners defining intent and requirements
• Engineers preparing specifications and implementation plans
• DevOps and Security teams supporting production adoption
• Build agentic tooling when it provides clear value, including:
• Custom skills
• Subagents
• Hooks
• Quality gates
• Evaluation frameworks
• Instrument the solutions you build to make cost, usage, and business outcomes visible.
• Run demos, workshops, and working sessions that help teams start using the tools immediately.
• Support teams until they can operate the solutions independently, then move on to the next AI-DLC focus area or engineering team.
• Occasionally participate in pre-sales activities as a technical lead by contributing:
• The proposed vision
• The delivery approach
• A realistic assessment of expected productivity gains

The balance between building and enablement

Approximately half of this role is hands-on engineering. The other half is making sure the solutions are adopted and deliver real value.

You may spend one day building a prototype independently or with a teammate, and the next day:

• Demonstrating it to a sceptical engineer
• Helping a Product Owner write their first AI-ready specification
• Running an enablement session
• Supporting a security review
• Presenting the expected business impact to client stakeholders

This role is best suited to someone who enjoys both creating solutions and helping others use them successfully.

Many clients already have access to tools such as Claude Code, GitHub Copilot, Cursor, or Kiro. The challenge is often not access to technology, but inconsistent adoption across teams. Sometimes the right answer is a custom agent. In other cases, the best solution is a clear, repeatable process that everyone can follow. You will be expected to understand the difference.

What we are looking for

• 5+ years of experience shipping production software, with strong hands-on involvement in recent projects.
• Experience building tooling around AI agents, not only using AI coding assistants.
• Experience teaching or introducing a solution to people who were not initially asking to learn it.
• Ability to communicate effectively with both sceptical engineers and executive stakeholders.
• Fluent English and confidence working directly with clients.
• Strong depth in at least one engineering area, such as:
• Backend
• Frontend
• QA
• DevOps
• Data

Technical environment

You are comfortable working with agentic coding tools such as:

• Claude Code
• Cursor
• GitHub Copilot
• Kiro
• Codex

More importantly, you have built inside or around these tools, for example:

• Custom skills
• Subagents
• Hooks
• Slash commands
• MCP servers
• Specification-to-implementation workflows
• Automated engineering processes that do not require constant manual supervision

You should also have strong experience with one programming language. We are open to:

• Python
• TypeScript
• C#
• Java
• Kotlin
• Go
• Or another comparable language

Additionally, you should be comfortable with:

• CI/CD
• Docker
• SQL
• At least one major cloud platform
• Git and disciplined code review practices

Useful but optional experience

The following experience is welcome but not required:

• Agent observability and tracing with OpenTelemetry, Langfuse, LangSmith, Phoenix, or similar tools
• Evaluation frameworks such as DeepEval, Ragas, or a custom evaluation harness
• LangGraph
• RAG and vector search
• DORA or SPACE metrics and productivity measurement

What this role does not involve

This is not a model development or MLOps role.

We are not looking for expertise in:

• Model training
• Fine-tuning
• Inference optimisation
• MLOps

Those areas are handled by other teams at N-iX. This role focuses on helping engineering teams become more effective with AI.

Show us something you have built

Please share something practical that demonstrates your experience. This could be:

• A repository
• An internal skill your team uses
• An evaluation harness
• An MCP server
• A dashboard tracking agent usage or cost
• An AI-enabled engineering workflow

It does not need to be large or production-ready. We would rather review a small solution that you can clearly explain than a CV listing dozens of frameworks.

If your best work is confidential or covered by an NDA, describe it at a high level and we will find another way to discuss it.

Our hiring process

1. Screening call — 30 minutes
2. Technical conversation with the team
3. Either:
• A walkthrough of work you have already built, or
• A short practical exercise, if that provides a clearer way to demonstrate your skills

Why join the GenAI Value Lab?

You will have a permanent place in the GenAI Value Lab within the CTO Office.

Approximately 20–40% of your time will be dedicated to research and development, giving you space to explore and develop ideas that have not yet been formally requested. The remaining time will be spent on client engagements, rotating between different engineering teams.

You will work with clients across fintech, healthcare, and industrial software while helping N-iX stay ahead of the market in practical AI adoption.