Senior Machine Learning Ops Engineer

About the role

The Senior Machine Learning Ops Engineer will design, implement, and maintain scalable machine learning infrastructure that supports production AI initiatives. This role requires strong engineering expertise, operational ownership, and collaboration across technical teams.

Responsibilities

• Design, deploy, and maintain scalable ML infrastructure supporting model training, batch processing, and real-time inference workloads.
• Build and manage cloud-based infrastructure and services using AWS, Snowflake, and related platforms through Infrastructure-as-Code practices.
• Create and improve CI/CD pipelines, automation frameworks, testing processes, and deployment standards for machine learning systems.
• Partner with Data Science and Data Engineering teams to productionize models and accelerate machine learning delivery.
• Establish monitoring and observability frameworks, including model performance tracking, drift detection, data quality monitoring, and automated alerting.
• Improve platform reliability, scalability, security, governance, and operational efficiency across ML workflows.
• Support architecture decisions, engineering standards, and best practices for enterprise ML platforms.
• Document technical architecture, deployment processes, and operational procedures to ensure maintainability and knowledge sharing.
• Contribute to the development of reusable ML infrastructure components and data products.
• Support both batch and low-latency inference workflows while optimizing system performance.
• Help define the future direction of machine learning operations and platform capabilities.

Requirements

• Bachelor’s degree in Computer Science, Data Engineering, or a related technical field; advanced degree preferred.
• 6+ years of experience in MLOps, platform engineering, DevOps, data engineering, or related infrastructure roles.
• 3+ years of hands-on experience working with AWS cloud infrastructure.
• Strong Python engineering skills, including API development, automation, and backend service development.
• Experience building and operating production machine learning systems.
• Strong knowledge of Docker, containerized application deployment, and modern deployment practices.
• Experience with Kubernetes, ECS, EKS, or similar container orchestration platforms.
• Experience managing Infrastructure-as-Code projects using tools such as Terraform, OpenTofu, or CloudFormation.
• Strong SQL skills and experience with modern data warehouse platforms such as Snowflake, Databricks, or BigQuery.
• Experience implementing CI/CD workflows and software engineering best practices.
• Experience with workflow orchestration tools such as Airflow, Dagster, or Prefect.
• Experience with testing frameworks such as pytest, including unit, integration, and end-to-end testing approaches.
• Strong understanding of Bash and Unix-based environments.
• Experience with backend frameworks such as FastAPI, Flask, or Django.
• Knowledge of ML observability and experiment tracking tools such as MLflow, Arize, Evidently, WhyLabs, or Monte Carlo is a plus.
• Experience designing feature stores or reusable ML data products is preferred.
• Experience supporting Generative AI, LLM deployment workflows, financial services, fintech, or regulated industries is a plus.
• Strong communication skills with the ability to collaborate effectively across Data Science, Data Engineering, Product, and technical teams.
• Able to manage multiple priorities, work independently, and thrive in a fast-paced environment.

Benefits

• Competitive salary range of approximately $150,500 to $173,000 annually, depending on location, skills, experience, and qualifications.
• Medical, dental, and vision insurance coverage.
• 401(k) retirement plan with company match.
• Paid holidays, vacation time, sick days, and volunteer time off.
• 12 weeks of paid parental leave.
• Pre-tax transit benefits.
• Company-paid life insurance.
• Voluntary benefits options.
• Discounted pet health insurance.
• Wellness incentive programs.
• Employee mentorship and leadership development opportunities.
• Remote work flexibility.