I need an experienced AI engineer

We are hiring a Senior AI Engineer to own and accelerate AI capabilities across the platform — from customer-facing features to internal agentic workflows and production-grade AIOps. This is a high-impact, hands-on role where you will shape the next generation of AI systems and directly influence customer experience, product velocity, and operational efficiency.

The Opportunity

You will lead development across three core pillars:

- AI Product Engineering – Ship core AI-powered features — intake, scheduling, session notes, billing automation, and more.
- AI Foundations / Enablement – Build reusable primitives, evaluation tooling, and golden paths.
- Agentic Workflows & AIOps – Create AI agents that automate operational work and support internal operations.

What You’ll Do

AI Product Engineering (Customer-Facing Features)

- Build AI features end-to-end: requirements → design → implementation → rollout → evaluation.
- Implement LLM-backed workflows including summaries, extraction, structured output, copilots, billing scrubbing, and guided automation.
- Collaborate across Product, Design, QA, and Clinical SMEs.
Own AI-powered improvements across: RCM, scheduling, intake, documentation, reporting, customer support automation.

AI Foundations / Enablement

- Create reusable AI primitives: prompt templates, agent patterns, tool schemas, safety guardrails, retrieval modules.
- Build evaluation harnesses and continuous regression testing.
- Enable developer productivity via AI coding tools and best practices.
- Establish “golden paths” for consistent and safe AI feature delivery.

Agentic Workflows & AIOps

- Develop in-platform agents for billing checks, eligibility validation, claim scrubbing, data cleanup, workflow routing, and anomaly detection.
- Build AIOps capabilities: incident summaries, RCA suggestions, intelligent alerts, correlation.
- Integrate with telemetry systems for intelligent operations.

LLMOps / Production-Ready AI

- Implement monitoring, cost governance, retries, fallbacks, and safe-mode behavior.
- Own prompt/model evaluation frameworks and online QA metrics.
- Ensure HIPAA-aligned data flows: privacy controls, redaction, audit logs, PHI boundaries.

Data & Infrastructure

- Build vector stores, embeddings, retrieval workflows, and knowledge bases.
- Partner with data engineering for eval datasets and rulesets.
- Contribute to scalable infrastructure for AI agents and async workflows.

Tech Stack

- AI Platforms: Azure OpenAI, OpenAI, Anthropic, Bedrock
- Frameworks: RAG, agents, orchestration
- Backend: .NET, Node.js, Python
- Data: SQL Server, Postgres, Redis, vector databases
- Observability: Grafana, Loki, Prometheus, OpenTelemetry
- Cloud: Azure, AWS, Docker, Kubernetes
- AI Dev Tools: Copilot, Claude Code, Cursor, Kiro

What You Bring

- 5–10+ years building production software systems.
- Hands-on experience delivering LLM-powered features or AI agents.
- Strong engineering fundamentals.
- Experience with LLMOps, safety, monitoring, evaluation.
- Strong cross-functional communication.
- Healthcare / HIPAA experience a plus.
- Fluent English