AI DevOps Engineer

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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.

We are hiring an AI Dev Ops Engineer to own how our AI systems get deployed, stay up, and stay observable. You will own the infrastructure and operational substrate for Barton Malow’s agentic platform — the CI/CD pipelines, the cloud environments, the release machinery, the reliability practice, and the telemetry layer that turns opaque agent behavior into something you can measure, alert on, and debug.

Our engineers build the AI agents; you make shipping them safe, repeatable, and observable. Right now most of our apps have no CI/CD, the infrastructure is fragmented, and the agentic systems being stood up have little more than print statements for observability. Building that operational foundation is the job.

Agentic systems fail differently from ordinary services — nondeterministic output, silent quality drift, runaway tool-call loops, and cost that spikes without warning. Standard Dev Ops is necessary but not sufficient. This role exists because someone has to own reliability and telemetry for systems that don’t fail the way the runbooks assume.

Own CI/CD for the platform. Build the pipelines that take AI systems from commit to production — automated testing, evaluation gates, security and dependency checks, controlled and canary releases, and one-command rollback. Most of our apps have no pipeline today; you will establish the pattern and make the safe path the default path.

Manage cloud infrastructure as code. Own the cloud environments (AWS primarily) the platform runs on — provisioning, networking, secrets, environment parity, and cost controls — as versioned, reviewable infrastructure-as-code, not hand-tuned consoles. You are accountable for environments that are reproducible, least-privilege by default, and cheap to stand up and tear down.

Run the reliability practice. Own production reliability: SLOs, on-call and incident response, capacity and cost management, self-healing loops that detect and recover from failures, and blameless post-incident review. You will help define what “up” and “healthy” even mean for a nondeterministic system.

Build the agent telemetry and observability layer. Instrument the platform so agent behavior is legible: structured traces of agent runs and tool calls, token and cost accounting, latency and success metrics, output-quality tracking over time, and the dashboards and alerts that surface a regression before a user does. When an agent misbehaves in production, the telemetry you built is how the team finds out and figures out why.

Set the operational standard by example. On a small, high-leverage team, your pipelines and dashboards are the template. You establish the deployment patterns others adopt, the observability every new system gets wired into by default, and the operational discipline that lets a lean team run production systems well.

• 6–8 years in Dev Ops, SRE, platform, or infrastructure engineering, with a track record of running production systems you were accountable for
• Deep CI/CD experience — you have built and owned pipelines (Git Hub Actions, Git Lab CI, or similar) that gate, test, and safely release real production software
• Strong cloud operations, ideally AWS — provisioning,…