Lead Software Engineer, AI

## Lead Software Engineer, AIApplyremote type:
Hybrid locations:
United States of America, Eagan, Minnesota:
United States of America, Frisco, Texas:
Canada, Toronto, Ontario:
United States of America, Ann Arbor, Michigan time type:
Full time posted on:
Posted 4 Days Agotime left to apply:
End Date:
September 29, 2026 (30+ days left to apply) job requisition :
JREQ
202372
** Lead Software Engineer, AI
** What if the AI agents you architect empower legal professionals to act with sharper judgment and greater leverage, using tools that are themselves AI-native and multiply what you and your team can ship?

Our agent platform team builds and ships using AI-native engineering practices: agentic coding tools that let a small, senior group move at a pace well beyond traditional engineering throughput. That's how this team builds the infrastructure, tooling, and observability our AI agents need to run reliably at scale, often several efforts 're also in the middle of rethinking how we deploy and operate agents in production, pushing quality and safety checks earlier in the pipeline and raising our bar for release velocity and stability.

As our Lead AI Engineer, you'll set technical direction across that portfolio, moving fluidly between the initiatives that matter most and shaping how a highly capable, senior team builds the next generation of legal AI agents.

About the Role In this opportunity as a Lead AI Engineer, you will:
* Set technical architecture and direction across the team's highest-priority initiatives, spanning core agent infrastructure, new internal tooling, and production reliability.
* Provide hands-on technical leadership wherever it matters most, flexing across the team's highest-priority initiatives rather than being locked into a single fixed workstream.
* Architect and build production AI agent systems on top of modern LLM APIs, agent SDKs, and orchestration/runtime infrastructure, designed to scale and integrate cleanly with existing platform services.
* Design agent observability and evaluation practices (tracing, automated evals, and quality/drift detection) so regressions in agent behavior get caught before they reach customers.
* Advance how the team deploys and operates AI agents in production, deepening practices like progressive/feature-flagged rollout and earlier-stage quality signals so releases get both faster and safer over time.
* Drive cross-cutting design reviews and technical decisions (RFCs/ADRs) to keep new systems coherent as multiple initiatives move in parallel.
* Mentor and raise the technical bar for a senior engineering team, including how they use AI-native, agentic development practices to multiply their own output.
* Partner with product and engineering leadership to translate strategic priorities into concrete system architecture and sequencing decisions.

About You You're a fit for the role of Lead AI Engineer if you have the following required qualifications:
* Extensive experience architecting and shipping production software systems, including hands-on experience building AI/LLM-powered products or agentic systems (not solely classical ML/data science).
* Direct experience building with modern LLM APIs, agent frameworks/SDKs, or agent-to-tool integration protocols.
* A track record of setting technical direction across multiple concurrent initiatives, not just delivering one project end-to-end.
* Strong backend engineering fundamentals, including distributed systems design, service architecture, and cloud infrastructure.
* Experience designing for production reliability and observability: monitoring, evaluation pipelines, incident response, and quality regression detection.
* Demonstrated ability to mentor senior engineers and influence technical culture and practices.
* Comfort operating with high autonomy on a fast-moving, senior-heavy team that builds with AI-native/agentic engineering practices.
** Additional preferred qualifications include:
*** Experience leading architecture for developer-facing platforms, plugin/extension systems, or integration ecosystems.
* Familiarity with progressive delivery practices (feature flagging, phased rollout, blue/green deployment) at scale.
* Experience pushing a team toward elite software delivery practices: progressive delivery, shift-left quality/testing, and the kind of engineering discipline reflected in metrics like deployment frequency, lead time, and change failure rate.
* Experience with managed agent runtime infrastructure (e.g., AWS Bedrock Agent Core or…