ML Platform Architect

2 days ago

Mumbai, India Philodesign Technologies Inc Full time

Job Title: MLOps Engineer Experience: 4–6 Years Work Mode: Remote Budget: ₹1 Lakh Per Month Employment Type: Full-time / Contract About the Role We are looking for an experienced Databricks MLOps Engineer to join our Data & AI Engineering team. The ideal candidate will have strong hands-on expertise in the Databricks ecosystem and a solid background in deploying, operationalizing, and maintaining machine learning models at scale. You will collaborate with data scientists, data engineers, and business teams to build robust, automated, and production-ready ML pipelines. Must-Have Skills - Databricks (Core Skill) - MLflow – Experiment tracking & model lifecycle management - End-to-End MLOps – Training, versioning, deployment, monitoring & retraining - Python – pandas, scikit-learn, PyTorch/TensorFlow - PySpark - Cloud Platforms – AWS (SageMaker), Azure or GCP - Docker / Kubernetes - CI/CD Pipelines – Jenkins, GitHub Actions, GitLab CI Key Responsibilities 1. Databricks Platform Management - Work with Databricks Workspaces, Jobs, Workflows, Unity Catalog, Delta Lake, and MLflow. - Optimize clusters, compute resources, and workspace permissions. 2. End-to-End ML Lifecycle - Implement and manage complete ML lifecycle: training, versioning, deployment, monitoring, and retraining. - Support deployment strategies like A/B testing, blue-green, and canary releases. 3. Programming & Development - Build scalable ML and data pipelines using Python, PySpark, and SQL. - Maintain code quality through Git, reviews, and automated testing. 4. Cloud & Infrastructure - Work across AWS/Azure/GCP for ML operations. - Implement IaC using Terraform. - Build/manage containerized workloads using Docker/Kubernetes. 5. CI/CD & Automation - Create and optimize CI/CD pipelines for ML workflows. - Automate data validation, feature generation, model training & deployment. 6. Monitoring & Observability - Implement monitoring using Databricks Lakehouse Monitoring for data quality, model drift, and performance. - Integrate explainability tools such as SHAP & LIME. 7. Feature Engineering & Optimization - Work with Databricks Feature Store. - Execute distributed training and hyperparameter tuning using Optuna, Ray Tune, etc. 8. Collaboration & Documentation - Collaborate with data scientists, ML engineers, DevOps, and business teams. - Create clear documentation & mentor junior engineers. Why Join Us? - Opportunity to work on large-scale AI & ML systems - Remote-first work culture - Exposure to modern cloud and MLOps technologies - Collaborative and growth-oriented environment How to Apply Interested candidates can share their updated resume at [priyanka.jadhav@philodesigntech.com] or apply directly via LinkedIn.

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