ML Ops Engineer

• Build, test, and validate ML pipelines that deliver actionable insights
• Work with various teams to explore different software tools and processes
• Create new products that significantly increase the value of the team
• Build scalable processes for large-scale data science models
• Develop ML Ops capabilities using open source and commercially available tools
• Follow engineering best practices that reduce the time to publish and improve model tracking in production
• Define and build an experimentation framework to facilitate strategies both for model experimentation and A/B testing
• Work on CI/CD pipelines that will impact the ML models
• Collaborate with data scientists/product owners and deploy ML models from development to production
• Communicate technical and business risks to relevant stakeholders
• Adapt to changing business needs and new information by pivoting your team to achieve promising initiatives
• Develop strong awareness of the organizational health
• Diagnose, troubleshoot, and make recommendations to solve complex issues on the platform

Job Requirements:

• Bachelor s/Master s degree in Engineering, Computer Science (or equivalent experience)
• At least 4+ years of relevant experience as a software engineer
• At least 2+ years of MLOps experience
• Demonstrated ability to ship complex, large-scale ML systems with high availability
• Extensive experience in developing ML code in Python using various open-source libraries
• Prolific experience with ML systems such as AWS SageMarker, Databricks, etc.
• Experience with design data flow, including ML-specific tasks such as feature generation, feature store, Model Observability, Experimentation, Model serving, etc.
• Nice to have experience with building CI/CD pipelines for ML models
• Prior experience working with Docker and Linux (preferably in Kubernetes) is a plus
• Experience with SQL and NoSQL databases, specifically related to building ML data pipelines, is preferred
• Nice to have experience with data pipeline orchestration tools such as Airflow, Dagster, and Prefect
• Experience in agile and test-driven methodologies
• Excellent verbal and written communication skills
• Strong analytical skills with proficiency in answering data-related questions
• Fluent in verbal and written English