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: 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 DatabricksOperationalize ML models, including deployment, monitoring, and lifecycle managementBuild and maintain CI/CD pipelines for ML workflowsDevelop and manage real-time and streaming data pipelinesCollaborate closely with Data Scientists to productionize models efficientlyImplement model versioning, experiment tracking, and reproducibilityDefine and enforce ML best practices, governance, and quality standardsMonitor model performance and data drift; implement automated retraining strategiesOptimize performance, scalability, and cost of distributed workloadsContribute 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 systemsProven experience designing and implementing CI/CD pipelinesStrong understanding of the ML lifecycle (training → deployment → monitoring → retraining)Experience building scalable, distributed data and ML pipelines Nice-to-Have Skills Experience with SnowflakeKnowledge of KuberneteExperience with DockerFamiliarity with Terraform or other Infrastructure as Code toolsExperience 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 APIsMonitoring and observability tools (ELK or similar)Familiarity with A/B testing / experimentation frameworksExperience with LLM deployment and servingKnowledge of RBAC, security, and governance in data/ML platformsExperience in cloud environments (Azure preferred) What Success Looks Like Fully automated, reliable ML pipelines from experimentation to productionHigh-quality, observable, and maintainable ML systemsStrong alignment between data science, engineering, and platform teamsScalable 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 systemsCustomer Personalization