Operations Machine Learning Engineer

Job Description:

• Convert machine learning models, analytical prototypes, and AI solutions into scalable production-ready workflows
• Design, build, and maintain automated data and model pipelines using Python, AWS, Databricks or similar cloud data platforms, Spark, and CI/CD practices
• Develop reusable deployment patterns, templates, documentation, and engineering standards
• Collaborate with Product & Technology, Data Engineering, Cloud Engineering, DevOps, Delivery Platforms, and business operations teams
• Create monitoring frameworks for model performance, data quality, drift, reliability, usage, and business impact
• Support responsible AI and model governance practices, including documentation, auditability, explainability inputs, validation workflows, and model health checks
• Develop feature engineering pipelines, curated data assets, model-serving workflows, and operational data products
• Identify and resolve issues, defects, or process gaps in machine learning pipelines, data workflows, and deployment automation
• Translate operational business needs into technical requirements
• Communicate technical concepts to non-technical stakeholders
• Participate in cross-functional working sessions and provide technical recommendations
• Help drive project execution across Data Science, business, and technology teams

Requirements:

• Bachelor's degree in computer science, data engineering, machine learning engineering, engineering, statistics, mathematics, data science, or another quantitative or technical field
• At least 3 years of relevant experience in machine learning engineering, MLOps, cloud platform engineering, data engineering, software engineering, or similar technical roles
• Experience building production-grade data or machine learning workflows in Python
• Strong background with cloud data technologies, preferably AWS and Databricks, Spark, or similar platforms
• Experience with CI/CD practices, deployment automation, version control, and software engineering fundamentals
• Knowledge of data engineering concepts, including data pipelines, data quality, orchestration, feature engineering, and scalable processing
• Demonstrated ability to partner with data science teams and translate analytical solutions into reliable operational workflows
• Ability to work independently, deliver high-quality work, seek input from others, and contribute to shared standards and best practices
• Strong communication skills, including the ability to explain technical concepts to business stakeholders and non-technical audiences
• Availability five days per week during designated work hours
• Ability to sit at a desk or workstation for extended periods, typically 7–8 hours a day
• Ability to operate a computer, use phone systems, type, and use multiple software programs simultaneously

Benefits:

• Medical insurance
• Dental insurance
• Vision insurance
• Life insurance
• Disability insurance
• 401(k) match
• Perks supporting employees, their families, and finances
• Career development opportunities
• Quarterly bonus
• Restricted stock unit grant based on individual performance
• Fully remote work from home or location of record within the U.S.
• No in-office requirements