A staff-level engineer who sets technical direction for agentic systems and then leads the work to ship them
You’ve built and operated agents in production, you know where they break, and you have opinions — held loosely, argued well — about how to build them so they hold up in a regulated environment
You operate with the scope of a domain owner, not a task owner
You take a problem that isn’t yet well-formed, define it, sequence it, and lead a group of engineers to a shipped and measured outcome
Your impact shows up in other people’s work as much as your own: the patterns they reuse, the design decisions they don’t have to relitigate, the ambiguity you removed before it cost the team a quarter
You treat AI two ways at once — as a product capability you build with judgment, and as a development multiplier you use fluently — and you’re the person who raises the org’s bar on both
Technical Leadership & Direction
Own the technical direction for a significant AI-native domain: agent architecture, platform abstractions, or evaluation and guardrail infrastructure
Act as tech lead for a squad or a cross-team initiative — decomposing ambiguous problems, sequencing delivery, identifying and clearing blockers, and keeping the team pointed at the outcome rather than the ticket
Write and review design docs; make and document the load-bearing architectural calls, including the ones where the answer is “not yet” or “buy, don’t build.”
Establish clear technical ownership where it’s currently diffuse, so decisions have a named owner and reviews don’t stall
Agent Architecture & Engineering
Design and build production AI agents incorporating retrieval, orchestration, policy-based routing, tool invocation, evaluation harnesses, and lifecycle observability
Set the standards for what “production-ready agent” means here — testability, rollback safety, cost ceilings, failure modes, human-in-the-loop boundaries — and hold the bar in review
Take on the hardest parts of the build yourself. This is a hands-on role; you are expected to be in the code
AI Platform Foundations
Build and extend the abstraction layers that let teams integrate AI capabilities cleanly and safely across our services
Define the shared libraries, patterns, and guardrails other teams build on, and drive their adoption — a pattern nobody uses isn’t a pattern
Treat responsible use of AI on sensitive data as a hard engineering requirement, and translate privacy, security, and compliance constraints into concrete architecture rather than deferring them
Cloud-Native Engineering
Own full-stack delivery in TypeScript/Node.js and Python: service and API layers, data-processing jobs, and the internal interfaces on top of them
Leverage modern cloud infrastructure, event-driven patterns, CI/CD, and observability to deliver scalable AI-native systems
Own deployment, monitoring, and troubleshooting in production, including on-call, and improve the operational posture of what you inherit
Stakeholder Engagement & Advisory
Partner directly with product, operations, clinical operations, and business leaders as both technologist and trusted advisor — helping define which use cases are worth building and which aren’t
Lead design sessions, proofs of concept, and build-with sessions alongside the people who’ll use the workflows, building trust and adoption as you go
Communicate trade-offs, risks, and recommendations clearly to technical and non-technical audiences, up to and including the executive team
Influence roadmap and prioritization with a clear-eyed read of technical risk, sequencing, and cost
Measure & Improve
Own the evaluation strategy for your domain: define the metrics, test harnesses, and evaluation plans that measure agent accuracy, latency, safety, and cost-effectiveness
Instrument the systems so their behavior is legible after the fact, not just at demo time
Iterate rapidly on data, feedback, and changing requirements — and kill approaches that aren’t working, early and visibly
Growing the Org
Mentor and grow engineers through code review, design review, pairing, and direct feedback; make the people around you measurably better
Craft reusable patterns, documentation, and best practices that raise the engineering bar beyond your own team
Anchor our internal community of practice around AI-native and agentic engineering
What success looks like
First 90 days: you have a working map of our AI platform surface area, have shipped something real, and have a point of view on where the leverage is
First 6 months: you own a domain outright, are leading a team’s technical direction within it, and there’s an evaluation story for the agents you’ve shipped
First year: patterns you established are in use by teams you don’t sit on, and engineers point to you as the reason their work got better
Benefits
Medical, vision, and dental insurance
Flexible PTO policy
$500 home office stipend
Flexible WFH policy
Paid parental leave
$5250 personal growth stipend- Fluency with relational data and SQL
A track record of technical leadership as an individual contributor: owning a domain, leading multi-engineer efforts to completion, and driving decisions across team boundaries without positional authority
A working practice of using AI development tools as a force multiplier, with judgment about when to trust, verify, or override them
8+ years building and operating production software, with meaningful full-stack depth across a TypeScript/Node.js backend and at least one other language (Python strongly preferred)
Strong cloud-native engineering fundamentals; comfort with CI/CD, observability, and running what you build
Demonstrated ability to take a loosely defined problem and drive it to a shipped, measured, agent-powered workflow
Comfort with ambiguity and a bias toward shipping measurable results
Hands-on experience designing and deploying agentic systems in production — retrieval, orchestration, tool/function calling, and evaluation — with a clear-eyed view of where LLMs and agents work and where they don’t
Clear written and verbal communication, including the ability to write a design doc that changes minds. This is a remote, cross-functional role
Experience with agent frameworks and multi-agent architectures at production scale
Model evaluation and guardrail infrastructure — measuring output quality, catching regressions, keeping agents inside safe bounds
Experience building platform capabilities consumed by other engineering teams
Background in workflow automation, forecasting-driven products, or supply-demand matching
Prior work in a regulated environment (healthcare/HIPAA, fintech, etc.) and an instinct for the constraints that come with using AI on sensitive data
Experience mentoring engineers or acting as a formal tech lead
