Senior MLOps Engineer

Accountabilities:
The Senior MLOps Engineer will lead the design, development, and improvement of the platforms and processes that support machine learning teams. This role focuses on creating scalable ML infrastructure, improving operational maturity, and enabling teams to deliver reliable AI solutions efficiently. Lead the development and evolution of MLOps platforms, workflows, and infrastructure supporting machine learning initiatives. Design and standardize processes for model development, training, deployment, monitoring, and lifecycle management. Partner with data scientists and engineers to improve ML workflows from experimentation through production operations. Establish and maintain model governance practices, including reproducibility, lineage tracking, access controls, and operational standards. Support ML tooling such as experiment tracking, model packaging, deployment workflows, and model promotion processes using platforms such as MLflow or similar technologies. Collaborate with multiple teams to onboard new ML use cases and improve shared machine learning capabilities. Identify and resolve infrastructure challenges related to scalability, reliability, performance, and cloud cost optimization. Build automation through infrastructure-as-code, CI/CD pipelines, and standardized engineering practices. Improve observability, monitoring, and operational processes for machine learning systems. Partner with data engineering and platform teams to align ML systems with broader data architecture and governance strategies. Create documentation and technical standards that improve consistency and efficiency across ML teams. Drive cross-functional improvements that increase the speed and reliability of production ML delivery. Requirements: The ideal candidate is an experienced MLOps or platform engineering professional with strong cloud expertise, a deep understanding of machine learning operations, and the ability to collaborate across technical teams. 5+ years of experience in MLOps, machine learning engineering, platform engineering, data engineering, or a related technical field. Strong hands-on experience managing production workloads in AWS and understanding of cloud infrastructure principles. Solid knowledge of the machine learning lifecycle, including model training, deployment, monitoring, and maintenance. Experience supporting production-grade ML workflows focused on reliability, scalability, and reproducibility. Familiarity with ML tooling such as MLflow, experiment tracking systems, model management platforms, or similar technologies. Experience with workflow orchestration, infrastructure-as-code, and CI/CD practices for ML or data platforms. Understanding of secure access patterns, governance controls, and shared cloud services supporting ML environments. Experience improving engineering standards and operational practices across multiple teams. Strong programming, automation, and infrastructure management skills. Ability to communicate effectively with both technical and non-technical stakeholders. Comfortable working independently in a distributed environment with a high level of ownership. Experience with Databricks is a plus. Experience supporting multiple data science teams through shared MLOps platforms is preferred. Experience implementing ML governance frameworks and operational controls at scale is a plus. Experience working in a fast-growing environment with evolving AI and platform needs is preferred. Benefits: Competitive salary and equity packages. Salary ranges based on location: Region 1: $172,550 – $203,000 Region 2: $158,950 – $187,000 Region 3: $147,900 – $174,000 Comprehensive health, dental, and vision insurance. Mental health resources and employee wellness support. Personal device benefit and employee discounts for family and friends. 20 days of paid time off. 13 paid holidays. 8 days of flexible wellness time off. Paid sick leave and parental leave. Remote work flexibility within eligible United States locations. Opportunity to contribute to impactful technology that improves people’s health and well-being. Supportive culture focused on collaboration, inclusion, and professional growth.

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