Machine Learning Engineer

Role Overview

As a Machine Learning Engineer on the AI Platform, you will design, build, and ship AI agents and automation to solve real problems across HealthEdge’s engineering, product, and delivery organizations, including customer-facing operations. You will partner with stakeholders (Engineering, Product, and healthcare professionals) to understand workflows, find high-leverage opportunities, and deliver end-to-end solutions.

Responsibilities

• AI Agent & Automation Development
  • Develop and implement AI agents and automation from problem discovery through prototyping, evaluation, hardening, and production deployment.
  • Build reusable assets for the AI platform, including libraries, prompt templates, tool-use patterns, and evaluation scaffolding.
• Integration with Software Infrastructure
  • Partner with software engineers to integrate AI into existing systems, ensuring seamless functionality and performance.
• Cross-Functional Collaboration
  • Work with product managers, implementation consultants, engineers, and business operations to identify pain points, scope solutions, and iterate toward measurable outcomes.
• Research & Continuous Learning
  • Stay current with advancements in LLMs, agentic AI frameworks, and healthcare technology and apply new ideas to team innovation.
• Performance, Reliability, and Safety
  • Optimize for accuracy, latency, cost, and safety, including human-in-the-loop design and appropriate guardrails for healthcare.
• Documentation
  • Maintain clear documentation of model development processes, methodologies, and results for transparency and reproducibility.

Required Qualifications

• Education: Master’s degree in Computer Science, Machine Learning, Data Science, or related field (or Bachelor’s with relevant experience).
• Experience: 2–4 years building and deploying ML/AI systems in production.
• Technical Skills:
  • Strong Python proficiency
  • Experience with LLM APIs
  • Agentic frameworks (e.g., LangChain, Strands) and prompt engineering
  • Traditional ML frameworks (PyTorch, scikit-learn)
  • Software engineering fundamentals: version control, testing, CI/CD, and comfort across the development lifecycle
• Healthcare Knowledge: Interest or familiarity with healthcare data, clinical workflows, and regulatory requirements (EHR experience is a plus but not required).
• Analytical & Communication Skills: Ability to solve complex problems with complex datasets and explain technical concepts to non-technical stakeholders.
• Builder Mindset & Teamwork: Comfortable in ambiguity, turns ideas into working solutions, and thrives in cross-functional collaboration.

Nice-to-Haves

• Experience working directly with non-technical stakeholders or in embedded/consulting-style roles
• Experience with electronic health records (EHR) or other healthcare datasets