Senior ML Operations Engineer

About the role

This position is responsible for the platforms, tools, and processes that take our models from ideas to production models, serving predictions in real time. The Sr. ML Ops Engineer will partner with our Data Science, Data Product Management, Product Engineering, and Data Platform teams to create and support tools and processes to automate model productionalization.

Responsibilities

• Designs, builds, and maintains scalable ML infrastructure and pipelines for model training, deployment, and monitoring.
• Optimizes orchestration processes to ensure efficient deployment and management of predictive models.
• Optimizes resource usage to minimize infrastructure expense while maximizing performance.
• Maintains the performance, security, and scalability of the ML infrastructure.
• Collaborates with data scientists and software engineers to streamline the ML lifecycle from development to production.
• Develops and maintains tools for data analysis, experimentation, model versioning, and artifact management.
• Supports data and model governance requirements as needed.
• Develops robust monitoring systems to measure and trend model performance, detect model drift, and ensure optimal performance of models in production.
• Develops automation scripts and tools to improve the efficiency and reliability of MLOps processes.
• Optimizes ML workflows for efficiency, scalability, and reliability.
• Provides technical assistance and mentorship to all team members; troubleshoots complex issues and escalates issues, as necessary.
• Supports the company commitment to risk management and protecting the integrity and confidentiality of systems and data.

Requirements

• Education and experience typically obtained through completion of a Bachelor's degree in Computer Science, Engineering, or a related field.
• Minimum 5 years’ experience in Data Science, ML Engineering or ML Ops capacity.
• Strong programming skills in Python and experience with Data Science and ML packages and frameworks.
• Experience with AWS services.
• Proficiency with containerization technologies (Docker, Kubernetes) and CI/CD practices.
• Experience deploying models with MLOps tools such as MLflow, Kubeflow, or similar platforms.
• Expert understanding of data management, distributed computing, and software architecture principles.
• Proven experience delivering real-time models in production environments.

Qualifications

• Additional related education and/ or work experience preferred.
• Experience in hybrid (OnPrem / Cloud) environments.
• Hadoop / Hive / Cloudera experience.
• Distributed computing programming skills such as Spark.
• Experience with Scala / Java programming languages.

Skills

• Strong programming skills in Python and experience with Data Science and ML packages and frameworks.
• Experience with AWS services.
• Proficiency with containerization technologies (Docker, Kubernetes) and CI/CD practices.
• Experience deploying models with MLOps tools such as MLflow, Kubeflow, or similar platforms.
• Expert understanding of data management, distributed computing, and software architecture principles.
• Proven experience delivering real-time models in production environments.

Benefits

• Healthcare Coverage – Competitive medical (PPO/HDHP), dental, and vision plans as well as company contributions to your Health Savings Account (HSA) or pre-tax savings through flexible spending accounts (FSA) for commuting, health & dependent care expenses.
• 401(k) Retirement Plan – Featuring a 100% Company Safe Harbor Match on your first 6% deferral immediately upon eligibility.
• Paid Time Off – Flexible Time Off for Exempt (salaried) employees, as well as generous PTO for Non-Exempt (hourly) employees, plus 11 paid company holidays and a paid volunteer day.
• 12 weeks of Paid Parental Leave.
• Maven Family Planning – provides support through your Parenting journey including egg freezing, fertility, adoption, surrogacy, pregnancy, postpartum, early pediatrics, and returning to work.