The Staff Engineer, AI will build AI agents designed for healthcare workflows. This role will focus on creating intelligent, context-aware agents capable of interacting seamlessly via protocols such as MCP (Model Context Protocol), A2A (Agent-to-Agent), or ACP (Agent Communication Protocol). The Engineer will collaborate with cross-functional teamsincluding product managers, data engineers, and clinical expertsto develop solutions that are HIPAA/HITECH compliant, clinically relevant, and technically robust.
- Engineer and deploy AI agents leveraging MCP, A2A, or ACP to support multiple use-cases in healthcare such as clinical and operational.
- Develop agent workflows to enhance the efficiency and accuracy of medical note-taking, chart summarization, coding, and other clinical documentation processes.
- Integrate GenAI, NLP, and computer vision models into healthcare applications, ensuring alignment with EMR/EHR systems.
- Collaborate with product teams, data engineers, and clinical stakeholders to align solutions with real-world healthcare workflows and standards.
- Evaluate and integrate multiple open-source and/or proprietary GenAI models, optimizing for cost, performance, and safety.
- Monitor, evaluate, and continuously improve model performance using feedback loops.
- Stay current with advancements in GenAI, autonomous agents, voice recognition, and healthcare AI technologies.
- Perform other duties that support the overall objective of the position.
Education Required:
- Bachelor's degree (or higher) in Computer Science, Data Science, Artificial Intelligence, or a related field.
- Or, any combination of education and experience that would provide the required qualifications for the position.
Experience Required:
- 5-8 years as a Data Scientist/AI Engineer with experience leading projects independently
- Experience with fine-tuning and deploying models in production environments.
- Experience with agentic tools such as LangChain, LangGraph etc
Knowledge, Skills & Abilities:
- Knowledge of: Generative AI frameworks and architectures (Transformers, LLMs, diffusion models). Machine learning frameworks (PyTorch, TensorFlow, Scikit-learn). Healthcare data formats, privacy requirements (HIPAA), and integration challenges. Voice recognition and transcription technologies (preferred)
- Skill in: Programming in Python (preferred) or similar languages. Designing modular, scalable AI agent architectures. Prompt engineering, retrieval-augmented generation (RAG), and model context optimization. Problem-solving and analytical thinking.
- Ability to: Rapidly prototype and iterate on AI agent designs. Collaborate effectively across technical and clinical teams. Adapt in a fast-paced, mission-driven environment. Communicate technical concepts to non-technical stakeholders.
