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