MLOps Engineer building production ML infrastructure, pipelines, and monitoring for exacare ai’s post - acute care AI platform. Scaling reliable systems that help healthcare teams make safer placement decisions.
About the role
- Senior Machine Learning Engineer developing ML infrastructure for DraftKings' Casino, Sportsbook, and Fantasy products. Collaborating with Data Science and Engineering teams to streamline workflows.
Responsibilities
- Lead the design and development of scalable ML infrastructure and data pipelines that support multiple Data Science teams across Marketing Intelligence
- Build reusable frameworks and workflows for feature engineering, model training, deployment, and backfilling at scale
- Design and maintain CI/CD systems for data and ML pipelines, enabling reliable and automated production deployments
- Improve system reliability through monitoring, alerting, and observability across data and ML workflows
- Partner with Data Scientists to productionize models, standardize workflows, and eliminate redundant engineering effort
- Optimize large-scale data processing workflows (e.g., Spark) for performance, cost, and reliability
- Drive improvements in developer experience through better abstractions, tooling, and shared patterns
- Mentor engineers and contribute to raising the team’s standards for system design, code quality, and operational excellence
Requirements
- Bachelor’s degree in Computer Science, Data Science, Statistics, Mathematics, or a related technical field
- Strong Python and SQL skills, with hands-on experience in distributed data processing (e.g., Spark)
- Experience designing and maintaining data/ML pipelines and orchestration workflows in production environments
- Experience with CI/CD for data or ML systems, including testing, deployment, and release management
- Experience working with cloud-based data platforms such as Databricks, Snowflake, or AWS (open to equivalent experience)
- Strong understanding of system design tradeoffs, including scalability, reliability, and maintainability
- Proven ability to deliver end-to-end solutions and operate them in production with a high sense of ownership
- Strong communication and collaboration skills, with experience working across Data Science and Engineering teams
Benefits
- Professional development opportunities
- Work from home flexibility
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