Senior MLOps Engineer

Requirements:

• Strong experience with Databricks (Workflows MLflow Delta Lake) Apache Spark (batch and streaming) and advanced Python (production-quality code).
• Hands-on experience with streaming and real-time data systems.
• Proven experience designing and implementing CI/CD pipelines.
• Strong understanding of the ML lifecycle (training deployment monitoring and retraining) and building scalable distributed data and ML pipelines.
• Experience with Snowflake Kubernetes and Docker.
• Experience with Terraform or other Infrastructure as Code (IaC) tools.
• Experience with feature stores (e.g. Snowflake Feature Store Databricks Feature Store) and event-driven architectures (e.g. Kafka).
• Experience with model serving frameworks low-latency API development and LLM deployment/serving.
• Experience with monitoring and observability tools (e.g. ELK stack or similar).
• Familiarity with A/B testing and experimentation frameworks.
• Strong knowledge of RBAC security and governance in data/ML platforms.
• Experience with cloud environments (Azure preferred).

Responsibilities:

• Design build and maintain production-grade ML pipelines on Databricks.
• Operationalize ML models including deployment monitoring and full 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 efficiently productionize models.
• Implement model versioning experiment tracking and ensure reproducibility.
• Define and enforce ML best practices governance and quality standards.
• Monitor model performance and data drift and implement automated retraining strategies.
• Optimize performance scalability and cost of distributed workloads.
• Contribute to platform design for low-latency inference and scalable model serving.