AI Engineering Manager: Lead Production Healthcare AI

Inovalon, Inc.; Bowie (MD); Hybrid; USD 180,000 - 210,000; Full time

Job summary

Inovalon, Inc. seeks a hands-on Manager of AI Engineering to lead a 5+ person team and deliver production AI systems at scale. You will guide architecture, review decisions, and partner with product, security, legal, and compliance to meet regulatory requirements in healthcare.

You will translate AI roadmaps into quarterly plans, drive end-to-end delivery, and balance accuracy, latency, and cost while mentoring engineers and collaborating with stakeholders.

Qualifications

• 10+ years in software engineering, ML, or data science.
• 3–5 years of people management leading 3–5 engineers/scientists.
• Bachelors or Masters in CS/AI/Data Science or related field.

Responsibilities

• Lead and coach an existing team of 5+ engineers through expectations and onboarding.
• Own end-to-end engineering execution: planning, delivery quality, incident response.
• Translate AI roadmap into quarterly plans with measurable outcomes.
• Collaborate with AI Product Managers to shape roadmap and use cases.
• Guide production AI architectures including ML, LLMs, RAG, OCR.
• Balance accuracy, latency, cost, safety, and scalability in designs.
• Review designs and prototypes; stay hands-on to unblock problems.
• Build reusable components and platforms for scalable AI capabilities.
• Ensure compliance with security standards, audits, and HIPAA needs.

Skills

Leadership

People mgmt

Agile/Scrum

Stakeholder comms

Education

Bachelors or Masters in CS/AI/DS or related

Tools

Python

SQL

CI/CD

Docker

Kubernetes

AWS

MLflow

Spark

Airflow

Job description

Inovalon, Inc. seeks a hands-on Manager of AI Engineering to lead a 5+ person team and deliver production AI systems at scale. You will guide architecture, review decisions, and partner with product, security, legal, and compliance to meet regulatory requirements in healthcare.

You will translate AI roadmaps into quarterly plans, drive end-to-end delivery, and balance accuracy, latency, and cost while mentoring engineers and collaborating with stakeholders.