Senior AI Engineer

Reports to

VP of AI Technology and Architecture

Job location

Must have ability to be onsite in Boulder , CO office for 2-3 days a week

About Colt and the AI Practice

Colt Technology Services is a global digital infrastructure company, operating one of the world's largest fiber networks across Europe and Asia. We are building a Boulder-based AI team as the engineering center of our AI Practice. The team is responsible for how AI is adopted, governed, and scaled across Colt.

The AI Practice operates across several parallel workstreams, including use case delivery, AI WAN, and private AI. The Boulder team is where we build, test, and validate before we scale.

Why we need this role

This is a build role. You will take AI use cases from a business problem to something running in production and being used every day: retrieval and search over Colt's own data, agents that carry out multi-step work, and AI built into the systems Colt's teams already rely on.

We are looking for a strong software engineer first. If you have spent eight to ten years building and running production systems and even if you have not yet shipped an LLM-based product, we want to hear from. The platform, the tooling, and the model access are already in place, and a staff-level AI engineer on the team will pair with you directly on the AI side of the work. The AI-specific skills you can bring to the table is a plus but is also learnable. The engineering experience and judgement that comes from having owned real systems in production is not, and that is the part we are hiring for. What we do expect is that you have already got hands on with AI, whether at work or as a side project for yourself.

You will be working directly with the Colt teams taking requirements from them. The team is small by design, the problems are real, and what you build in your first year sets the pattern for how Colt builds AI.

What you will do

Build AI Use Cases

• Design and build AI applications end to end: data access, retrieval, model calls, orchestration, and the interfaces people use
• Work with large language models through APIs and self-hosted inference, covering prompting, tool use, retrieval, and multi-step agent workflows
• Build evaluation harnesses that show whether a use case is good enough to ship, and keep owning them after it ships
• Integrate AI into Colt's existing systems and data, within the constraints of a live enterprise environment

Work Directly With the Business

• Work with the Colt teams, who will be your customers, and who will use what you build, across network operations, service delivery, finance, and others
• Turn a loosely described business problem into a scoped, buildable design
• Show working software early and often, and change direction when the first answer turns out to be wrong
• Be straight with stakeholders about what AI is not the right tool for

Take It to Production and Keep It There

• Move your own work from prototype to production-grade: tested, instrumented, documented, and supportable
• Monitor accuracy, latency, and cost in production, and act when they drift
• Work with platform engineers on deployment, and hand over cleanly to production operations
• Contribute reusable patterns, components, and internal libraries so the next use case is faster than the last

What we're looking for

• Engineering Experience: 8-10+ years building production software. You have owned systems end to end: designed them, shipped them, operated them, and been on the hook when they broke.
• Languages: Strong in Python, or strong in another language such as Java, Go, C#, or TypeScript and ready to work primarily in Python.
• Production Systems: APIs and service integration, working with real data at scale, testing, CI/CD, and debugging things that are already live in front of users.
• Cloud: Comfortable in any major cloud. We build primarily on Google Cloud; equivalent AWS or Azure experience is ok to start.
• Working Style: Comfortable with ambiguity and with direct contact with non-technical stakeholders. You ask what the problem is before deciding what to build.
• Hands-On with AI: You have already built something with it under your own steam. A side project, a prototype at work, an internal tool, a serious evaluation you ran, or agentic coding tools you use daily and have opinions about. It does not need to have shipped, been finished, or worked. We want to hear what you tried, what broke, and what you concluded.
• Helpful, Not Required: Any of this at enterprise scale: production RAG over messy real-world data, agent frameworks, evaluation tooling, Vertex AI, Kubernetes, Terraform. Useful if you have it, and teachable if you do not.

HOW YOU WILL RAMP

• You pair with a staff-level AI engineer from your first week, on a live use case rather than a training exercise
• The AI platform is already running: model gateway, evaluation tooling, deployment pipelines, and sandbox, development, and production environments
• We expect you to put something small into real use inside your first 90 days, and to lead a use case end to end within six months

WHEN WE TALK

• Come ready to walk us through something you have built with AI, however small or unfinished. We would rather see a rough prototype you can explain in depth than a polished project you cannot.
• Expect us to ask where it fell short, what you would do differently, and how you decided whether it was any good. How you evaluated it matters more to us than what you shipped.
• If you have not used AI tools in your own engineering work, this will be a difficult conversation. That is the one gap we are not set up to close for you.