Senior MLOps Engineer

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:

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