Machine Learning Operations Engineer

About Definitive Healthcare: 

At Definitive Healthcare (NASDAQ: DH), we’re passionate about turning data, analytics, and expertise into meaningful intelligence that helps our customers achieve success and shape the future of healthcare. We empower them to uncover the right markets, opportunities, and people—paving the way for smarter decisions and greater impact. 

Headquartered just outside of Boston, Massachusetts, Definitive Healthcare operates across North America, Europe, and India, supporting a growing global client base of more than 2,400 customers since our founding in 2011.

We’re also a great place to work. In 2024 and 2025, we earned multiple workplace honors, including Built In’s 100 Best Places to Work in Boston (both years), a Stevie Bronze Award for Great Employers, and recognition as a Great Place to Work in India.

We foster a collaborative, inclusive culture where diverse perspectives drive innovation. Through programs like DefinitiveCares and our employee-led affinity groups we strive to promote connection, education, and inclusion. 

About the Role

We are seeking a hands-on Principal MLOps Engineer & Functional Lead to architect, build, and scale our next-generation Machine Learning and Generative AI infrastructure. In this role, you will design a unified, automated, and secure AI ecosystem capable of training and deploying both traditional predictive models and advanced Generative/Agentic AI systems at enterprise scale.

As the Functional Lead, you will drive engineering excellence, establish MLOps best practices across the organization, mentor senior engineers, and translate strategic business objectives into robust, highly scalable automated pipelines.

What You’ll Do 

• Design, implement, and maintain scalable, robust ML pipelines and data pipelines for CI/CD, and ML model deployments.
• Architect and govern the enterprise feature store and vector store infrastructure to support low-latency feature retrieval for both traditional ML and Retrieval Augmented Generation (RAG) applications.
• Build and manage enterprise-grade workflow orchestration layers to schedule, monitor, and manage complex, multi-stage model training and data processing dependencies.
• Be a subject matter expert for Generative AI infrastructure, building scalable frameworks to support advanced LLM applications, RAG pipelines, and multi-agent Agentic AI workflows.
• Establish highly efficient environments for model training and parameter-efficient fine-tuning of open-source and proprietary models.
• Optimize high-throughput, low-latency model inference pipelines, utilizing advanced caching and compute distribution techniques to handle large-scale concurrent requests.