Principal Machine Learning Operations Engineer

About the role

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.

Responsibilities

• 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.
• Establish and enforce lifecycle management policies utilizing an enterprise model registry to manage version control, lineage, and tracking from experimentation to production.
• Architect high-performing, cost-efficient infrastructure utilizing autoscaling groups on AWS to dynamically handle varying training and inference workloads.
• Implement comprehensive, real-time endpoint monitoring systems to track model performance, data drift, latency, and system health metrics.
• Serve as the functional lead for ML engineering, defining coding standards, architectural blueprints, framework selections, and operational SLAs.
• Provide technical guidance, code reviews, and mentorship to senior machine learning and data engineers, fostering an agile culture of innovation, automation, and continuous improvement.

Requirements

• Bachelor's or Master’s degree in Computer Science, Computer Engineering, Data Science, or a closely related quantitative engineering discipline.
• 12+ years of professional experience in data engineering, ML engineering with a minimum of 5 years dedicated to building, scaling, and managing MLOps platforms in production.
• Deep, hands-on production experience architecting with Databricks and native AWS environments.
• Expert proficiency in Python, with strong production experience deploying models built in TensorFlow and PyTorch.

Pay

The salary range for this position is $158,000 – $294,000 per year, which represents the base pay the company reasonably and in good faith expects to pay for this role.

Actual pay within this range will be determined based on factors such as relevant experience, skills, and qualifications.

Depending on the