AI Engineer
(Hybrid) APEX — AI & Architecture
About Barton Malow
Barton Malow is a builder. For over 100 years we have delivered some of the most complex construction projects in North America — schools, hospitals, stadiums, manufacturing plants, and industrial facilities. Today we are doing something most construction companies are not: rebuilding how we build software, with AI at the center. Our APEX team owns the AI and engineering platform that the rest of the company builds on, and we are investing seriously in it.
About the role
We are hiring an AI Engineer to be a core builder on our agentic platform. You will build the agent pipelines, tool integrations, and production features that make AI agents do real, reliable work across Barton Malow — working closely with senior engineers who set the patterns you build against.
This is a hands-on role for an engineer who wants to build production AI systems, not just demos. You will own features end to end: from a working prototype to a component running against real Barton Malow data and workflows. You will learn the failure modes of AI systems by operating them, get your code reviewed at depth, and steadily take on harder and harder parts of the platform.
This is not a role at a software company, and it is not a role where you write a little code on the side. It is a role helping build the foundation an entire organization will run AI on for the next decade. The portfolio is messy, the problems are real, and the path to grow is clear.
What you’ll do
Build agent pipelines and integrations. Implement the working pieces of our agentic systems — agent pipelines, the tool and data integrations that connect agents to Autodesk, SAP, Databricks, and other systems, and the paths agents call to get real work done.
Ship to production and keep your systems healthy. Take your work from prototype to production: deployment, monitoring, controlled release, and fixing what breaks. The bar is not a demo that runs once — it is a component running reliably against real workflows, with you accountable for the parts you ship.
Write tests and evaluation checks. Verification is what separates a demo from production. You will write the tests, contract checks, and evaluation cases that prove your agents produce correct output before it ships — and the regression checks that catch it when their behavior drifts.
Build against the standard — and help improve it. You work inside patterns set by senior engineers. Building well against them is most of the job; the other part is flagging honestly, with a concrete example, when a pattern doesn’t fit what you’re building. That feedback is how the platform gets better.
Grow into the harder work. This is a growth role. You will pair with senior engineers and steadily take on more of the core platform — a harder integration, an evaluation gate, a piece of the orchestration loop.
What we’re looking for
- 4–6 years in software engineering, with a track record of shipping features to production and owning them afterward
- Hands-on experience building with LLMs or AI agents — you have built something real with an agent framework, an LLM API, or a tool-calling system, beyond using AI in your own workflow
- Strong software engineering fundamentals — testing, version control, code review, debugging — and the discipline to apply them even when the output is nondeterministic
- Comfort in a cloud environment (AWS preferred): you can deploy, monitor, and troubleshoot a service you built
- Some data or integration experience: calling SaaS APIs, moving data between systems, or working against a data warehouse or lakehouse
- A builder’s instinct: you ship the working thing, iterate in production, and ask for help before spinning for a day
Nice to have
- Hands-on experience with an agent framework or SDK
- Experience writing tests or evaluation cases for AI or LLM output
- Experience with Databricks or a similar lakehouse platform
- Experience building or consuming tool-integration layers (e.g., MCP servers) between AI systems and enterprise applications
- Experience in a non-software-company engineering organization — internal tools, corporate IT, or similar
- Public work — open source, a side project, anything that shows how you build
Why this role
You will build production AI systems at the moment an organization decides to invest seriously in them — with senior engineers and an architect as people you learn from, not gatekeepers who block you. The platform is early, and that is the appeal. You are not maintaining someone else’s infrastructure; you are helping build the platform Barton Malow will run on for the next decade, and growing into owning the hardest parts of it.
Barton Malow is an equal opportunity employer, and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, disability status, genetic information, protected veteran status, or any other legally protected characteristic.