Job Description

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 teams—including product managers, data engineers, and clinical experts—to 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.