MLOps Engineer

Job Summary:

We are looking for a seasoned MLOps Engineer to operationalize and scale machine learning solutions across the enterprise. The ideal candidate will have deep expertise in deploying ML models in production environments using tools like MLflow, managing large-scale data infrastructure on Cloudera/Hadoop, and enabling collaboration between data science and engineering teams through CI/CD pipelines on Azure DevOps. Familiarity with Knowledge Graphs and enterprise data lineage is a strong advantage.

Key Responsibilities:

• Design, implement, and manage end-to-end ML pipelines, ensuring scalability, reliability, and reproducibility.
• Build robust CI/CD pipelines using Azure DevOps to support model training, testing, deployment, and monitoring.
• Integrate model tracking, versioning, and lifecycle management using MLflow.
• Work with Cloudera and Hadoop ecosystem tools (HDFS, Hive, Spark) to handle large-scale data ingestion and transformation for ML use cases.
• Develop and manage Knowledge Graph pipelines to enrich metadata and model dependencies.
• Collaborate with data scientists to productionize models and ensure governance, auditability, and traceability.
• Implement monitoring solutions for model drift, performance, and data quality in production environments.
• Support infrastructure automation and DevOps practices aligned with enterprise security and compliance standards.
• Ensure alignment with data governance, privacy regulations, and organizational best practices.