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Enboarder is seeking a Machine Learning Engineer to build autonomous, conversational AI agents for a patient engagement platform. You will design systems that act on tasks with minimal human intervention while upholding safety, empathy, and accuracy.
You will work on agent development, evaluation, and observability, ensuring robust guardrails and HIPAA compliance across production environments.
We are seeking a Machine Learning Engineer to build the “active brain” of our patient engagement platform. In this role, you will develop autonomous and conversational AI agents capable of triaging patient messages and managing clinical workflows.
Your mission is to design systems that don’t just respond, but act—executing complex tasks with minimal human intervention while maintaining high standards of safety, empathy, and accuracy.
Autonomous Agent Development
Design and deploy multi‑agent systems using frameworks such as LangGraph, AutoGen, CrewAI, or no‑code/low‑code agent frameworks to execute complex, non‑linear clinical workflows.
Patient Engagement Agents
Build and optimize conversational AI interfaces that handle message triage, sentiment analysis, and context‑aware responses that are natural, empathetic, and patient‑centric. Develop tool‑calling architectures that enable AI agents to interact with external APIs, EHR systems, and databases to perform real‑world actions.
Model & Agent Evaluation
Design and implement evaluation frameworks to measure model and agent performance, including accuracy, reliability, safety, and task completion. Continuously improve systems using structured evaluation methodologies and feedback loops.
Safety & Guardrails
Implement robust evaluation pipelines and guardrails (e.g., NeMo Guardrails) to ensure agent safety, clinical accuracy, and HIPAA compliance.
AI Observability
Create and maintain dashboards to monitor agent reasoning traces, system performance, uptime, and task completion rates in production environments.
3+ years of experience in Machine Learning with a strong focus on AI agents and conversational NLP systems.
Proven experience building autonomous systems using LangGraph, AutoGen, CrewAI, or similar no‑code/low‑code agent frameworks.
Hands‑on experience with fine‑tuning models and implementing rigorous evaluation frameworks to ensure agent performance, safety, and reliability. Familiarity with techniques for detecting hallucinations, bias, and failure modes in agent behavior.
Strong understanding of guardrails, alignment techniques, and safety mechanisms to ensure compliant, accurate, and trustworthy agent outputs in sensitive environments.
Hands‑on experience with AWS and Databricks, including building CI/CD pipelines for deploying machine learning models and AI agents.
Strong proficiency in Python and experience with modern evaluation frameworks such as RAGAS or DeepEval.
At ModMed, we believe it’s important to offer a competitive benefits package designed to meet the diverse needs of our growing workforce. Eligible Modernizers can enroll in a wide range of benefits: