Sr. AI Engineer

About the role

The Senior AI Engineer will build the systems that make AI agents do real, reliable work across Barton Malow. This is a deep, hands-on, senior individual-contributor role.

Responsibilities

• Build the harness. Implement the core machinery that makes AI agents reliable: the orchestration loop, the tool-execution and integration layer, evaluation gates, state persistence, error recovery, and the observability that makes agent behavior legible.
• Ship agentic systems to production — and keep them reliable. Take AI systems from prototype to production: deployment, monitoring, rollback, controlled release, and the self-healing loops that detect and recover from failures.
• Build the evaluation and verification infrastructure. Verification is what separates a demo from production. You will build the evaluation harness, the regression suites, the deterministic gates (tests, linters, contract checks), and the automated judging infrastructure that lets us prove AI output is correct before it ships.
• Build the platform substrate. Implement the production components that connect AI agents to Barton Malow's real systems and data — Autodesk, SAP, Databricks, and a growing set of external services — and the interfaces people use to work with those agents.
• Set the engineering bar by example. As one of the most senior engineers on the team, you set the implementation patterns, write the reference code others build on, review at depth, and raise the quality of everyone working alongside you.

Requirements

• 10+ years in software engineering, with demonstrated depth as a senior individual contributor who ships complex systems to production
• Hands-on experience building and operating production AI or agentic systems — not just using AI tools in your own workflow. You have taken an LLM- or agent-powered system to production and kept it running
• Experience implementing the components behind reliable AI systems: orchestration, tool/function calling, evaluation, context management, and handoffs between steps or agents
• Strong software engineering fundamentals — testing, CI/CD, observability, error handling — applied to the realities of nondeterministic AI systems
• Production cloud operations, ideally AWS: architecting, deploying, and operating real workloads
• Data and integration experience: building pipelines that move data reliably between enterprise systems (ERP, SaaS APIs, data warehouses or lakehouses)
• A pragmatic builder's instinct: you build the reusable thing, iterate in production, and know when shipping behind a flag beats perfecting on paper
• Strong communicator who can work with engineers, managers, and executives and adjust the level for each

Qualifications

• Nice to have:
• Hands-on experience with an agent framework or SDK and with multi-agent or planner/generator/evaluator architectures
• Experience building evaluation frameworks or automated AI-judging systems
• Experience with Databricks or a similar lakehouse platform
• Experience building 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 transformation, or similar
• Open-source contributions or public work that shows how you build

Skills

• None specified

Benefits

• None specified

Pay

• Not specified

Schedule

• Not specified