Note: The job is a remote job and is open to candidates in USA. Onpoint Healthcare Partners builds Iris, a Medical Agent AI platform that enhances administrative efficiency for healthcare providers. They are seeking a Staff AI Engineer to design, build, and scale the systems behind Iris, ensuring that AI solutions deliver measurable business value and improve patient care.

Responsibilities

• Design and develop AI applications and agent workflows
• Build scalable backend services, APIs, and integrations supporting AI solutions
• Evolve and steward the agent platform architecture, including orchestration, runtime safety, and prompt governance
• Treat prompts and tool schemas as versioned code with staged rollouts and rollback
• Develop and maintain evaluation frameworks for AI agents and models, with CI gates that block bad changes before release
• Design retrieval, memory, and context management strategies
• Build observability, monitoring, and debugging capabilities, including full trace and replay for incident resolution
• Build the MLOps foundation the team is currently missing: training and retraining pipelines, model versioning, model registry, and feature stores, so model work stops being reactive
• Stand up A/B testing infrastructure and automated drift detection that triggers retraining, so model quality is monitored and maintained without manual firefighting
• Track token and tool costs per run, workflow, and tenant, and keep costs predictable as usage scales
• Improve reliability, performance, safety, and cost efficiency of production AI systems
• Partner with Product, Clinical, Data Science, and Engineering teams to deliver AI capabilities
• Own AI deployment, monitoring, and operational excellence

Skills

• 8+ years of software engineering experience
• 3+ years building AI and ML applications
• Strong Python and/or C# development experience
• Experience deploying AI systems into production environments
• Experience with LLMs, RAG architectures, agent frameworks, and AI evaluation
• Experience with AWS and distributed systems
• Hands-on experience building MLOps infrastructure such as training pipelines, model registries, feature stores, A/B testing, and drift detection
• Strong debugging, observability, and operational skills
• Healthcare industry experience
• Experience with HIPAA, PHI, and regulated environments
• Experience with vector databases, embeddings, and knowledge graphs
• Experience building AI systems at large scale

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