About this role
Our team members are the key to our company’s success, and their health and well-being, as well as that of their families, is very important to us. We offer a comprehensive benefits package that allows our team members stay healthy, plan for their future and maintain a healthy work-life balance. Benefits may vary with employment status. To see our fill list of Team Member Benefits please visit our career site: www.gotoworkhappy.com/benefits
Job Description: We are looking for a highly skilled MLOps Engineer to support the end-to-end machine learning lifecycle, from experimentation to production deployment. This role focuses on building scalable, reliable, and automated ML infrastructure, enabling data science teams to deliver production-ready models efficiently and confidently.
Key Responsibilities
• Design, build, and maintain production-grade ML pipelines on Databricks • Operationalize ML models, including deployment, monitoring, and lifecycle management • Build and maintain CI/CD pipelines for ML workflows • Develop and manage real-time and streaming data pipelines • Collaborate closely with Data Scientists to productionize models efficiently • Implement model versioning, experiment tracking, and reproducibility • Define and enforce ML best practices, governance, and quality standards • Monitor model performance and data drift; implement automated retraining strategies • Optimize performance, scalability, and cost of distributed workloads • Contribute to platform design for low-latency inference and scalable serving
Required Qualifications (Must-Have)
• Strong experience with Databricks (Workflows, MLflow, Delta Lake) • Deep expertise in Apache Spark (batch and streaming) • Advanced Python skills (production-quality code) • Hands-on experience with streaming / real-time systems • Proven experience designing and implementing CI/CD pipelines • Strong understanding of the ML lifecycle (training → deployment → monitoring → retraining) • Experience building scalable, distributed data and ML pipelines
Nice-to-Have Skills
• Experience with Snowflake • Knowledge of Kubernete • Experience with Docker • Familiarity with Terraform or other Infrastructure as Code tools • Experience with feature stores (e.g. Snowflake or Databricks Feature Store, etc.) • Experience with event-driven architectures (Kafka) • Experience with model serving frameworks and low-latency APIs • Monitoring and observability tools (ELK or similar) • Familiarity with A/B testing / experimentation frameworks • Experience with LLM deployment and serving • Knowledge of RBAC, security, and governance in data/ML platforms • Experience in cloud environments (Azure preferred)
What Success Looks Like
• Fully automated, reliable ML pipelines from experimentation to production • High-quality, observable, and maintainable ML systems • Strong alignment between data science, engineering, and platform teams • Scalable infrastructure that supports both batch and real-time workloads
Example Use Cases You Will Support
• Recommendation Systems (real-time / near real-time customer personalization) • LLM-based Products, including Text-to-SQL systems • Customer Personalization