Senior Engineering Manager, AI Platform

Open Loop was co-founded by CEO, Dr. Jon Lensing, and COO, Christian Williams, with the vision to bring care anywhere. Our telehealth support solutions are thoughtfully designed to streamline and simplify go-to-market care delivery for companies offering meaningful virtual support to patients across an expansive array of specialties, in all 50 states.

Open Loop is building the future of healthcare infrastructure. We're hiring a Senior Engineering Manager to build and lead our Applied AI team, the engineers who ship the LLM-based agents and assistants our operations, clinical and patient-facing teams use every day.

This is a leadership role with real technical depth. You'll hire and grow a team of Senior, Staff and Senior Staff Applied AI Engineers, own delivery of production AI systems, and set the standards for how we build, evaluate and safely run AI on patient data. You don't need to write production code daily, but you need to be credible in architecture and eval reviews and know what good looks like.

• Build & Lead the Team: Hire, onboard and develop a team of Applied AI Engineers across Senior, Staff and Senior Staff levels, and grow it from a single pod into multiple teams as the work expands.

• Own Delivery of Production Agents: Drive the roadmap and execution of LLM-based agents and assistants that real users rely on, from scoping to launch to ongoing operation.

• Set the Technical Bar: Partner with Staff+ engineers on architecture for agent runtime and orchestration, retrieval, model access and observability. Make the call on trade-offs when the team needs one.

• Evaluation as a Standard: Make evaluation sets, regression tests and AI-graded evals a required part of how the team ships, so we can show improvement with numbers and catch regressions before users do.

• Reliability, Cost & Operations: Own the operational health of AI systems in production, including on-call, incident response, provider fallbacks, model version upgrades and AI spend by agent and use case.

• Security, Privacy & Safety: Make sure every AI system protects patient data (PHI), follows least-privilege access, and defends against misuse such as prompt injection. Partner with Security and Compliance on HIPAA requirements.

• Workflow & Use Case Prioritization: Work with Product and Operations leaders to find where agents can reliably take over human workflows, where they should hand off to a person, and which bets to make first.

• Cross-functional Leadership: Explain AI capabilities, risks and timelines in plain language to product, operations, clinical and executive stakeholders, including when the honest answer is "the AI isn't ready for this yet."

• Team Health & Process: Run planning, prioritization and delivery rituals that keep a senior team shipping fast without burning out. Give clear, direct feedback and build career paths.

• Other duties as assigned.

• Bachelor's degree in Computer Science, Engineering, or a related technical field

• Proven Engineering
  
  Experience:
  
  10+ years of software engineering experience, including 3+ years managing engineers directly (hiring, performance management, career development).

• Production LLM
  
  Experience:
  
  Led or built LLM-based systems in production that real users relied on, not only demos, prototypes or personal projects.

• Senior Team Leadership: Experience managing senior and Staff-level engineers and earning their technical trust.

• Technical Depth: Enough hands‑on understanding of agent runtime, evaluation, retrieval and observability to review designs, challenge assumptions and make sound architecture calls.

• Evaluation Rigor: You hold teams to measuring whether AI systems work with evaluation data, rather than "it seemed to work."

• Engineering Fundamentals: Strong grounding in production engineering across testing, services, APIs, deployment, incident management and on-call.

• Data & Security Fluency: Experience leading teams that handle sensitive data with a security-first mindset.

• Comfort building in a greenfield space, and clear communication with technical and non-technical partners

• Regulated-Industry
  
  Experience:
  
  Healthcare (PHI, HIPAA), finance or insurance.

• GCP
  
  Experience:
  
  Leading teams that run production services on Google Cloud.

• Scaling Teams: Grew an engineering team from zero, or from one team to several.

• Model Operations: Led migrations between model versions or providers.

• Classic ML Background: Machine learning experience beyond LLMs.

• Internal Platforms: Led a…