MLOps Engineer

1 week ago

Nashik, India Hadron Talent -Hadronfinsys Full time

MLOps Engineer - Databricks Client: A large global enterprise (name not disclosed) Location: India Work Model: 100% Remote Contract: 6 months (initial) with possibility of extension Start Date: ASAP Engagement: Full-time / Long-term contract Role Overview We are seeking an experienced Databricks MLOps Developer to design, build, and manage scalable machine learning operations on the Databricks Lakehouse Platform. The role involves automating ML workflows, operationalizing models, enabling reproducible pipelines, and ensuring governance and monitoring across the ML lifecycle. Key Responsibilities 1. Develop Scalable MLOps Pipelines - Build automated ML pipelines for training, validation, deployment, and batch/real-time inference. - Use Databricks Workflows, Jobs, Repos, and Delta Live Tables where applicable. - Implement distributed training and inference pipelines using MLflow + PySpark. 2. Model Lifecycle Management - Manage model versioning and promotion across dev staging production using MLflow Model Registry. - Create reproducible workflows for model packaging, deployment, and rollback. 3. CI/CD Integration - Build and integrate ML pipelines with CI/CD using Azure DevOps, GitHub Actions, or Jenkins. - Automate testing, validation, and deployment for ML artifacts, notebooks, and infrastructure. 4. Feature Engineering & Data Pipelines - Collaborate with Data Engineering teams to build optimized Delta Lake pipelines (Bronze/Silver/Gold architecture). - Implement feature engineering workflows and support feature reuse at scale. 5. Monitoring & Governance - Set up model monitoring for performance, drift, data quality, and lineage. - Use Databricks-native tools, MLflow metrics, and cloud monitoring services (Azure/AWS). - Ensure compliance through logging, auditing, permissions, and environment governance. 6. Cross-Functional Collaboration - Work closely with Data Scientists, Data Engineers, Cloud teams, and Product teams. - Document workflows, best practices, and MLOps reusable components. Required Skills & Qualifications - Strong hands-on experience with Databricks (Workflows, Repos, Jobs, Compute) - Proficiency with MLflow (Tracking, Registry, Model Deployment) - Expertise in Delta Lake, PySpark, and distributed data pipelines - Solid programming skills in Python and SQL - Experience with CI/CD tools: Azure DevOps, GitHub Actions, Jenkins - Familiarity with cloud platforms: Azure, AWS, or GCP - Understanding of containerization (Docker) and orchestration (Kubernetes) - Background in ML model training, serving, and observability Preferred Qualifications - Databricks certifications: - Databricks Certified Machine Learning Professional - Databricks Certified Data Engineer Associate/Professional - Experience with Unity Catalog for governance - Experience implementing feature stores - Knowledge of ML observability tools (WhyLabs, Monte Carlo, Arize AI, etc.)

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