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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