ML Platform Architect

Job Title : MLOps EngineerExperience : 4–6 YearsWork Mode : RemoteBudget : ₹1 Lakh Per MonthEmployment Type : Full-time / ContractAbout the RoleWe 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 SkillsDatabricks (Core Skill)MLflow – Experiment tracking & model lifecycle managementEnd-to-End MLOps – Training, versioning, deployment, monitoring & retrainingPython – pandas, scikit-learn, PyTorch / TensorFlowPySparkCloud Platforms – AWS (SageMaker), Azure or GCPDocker / KubernetesCI / CD Pipelines – Jenkins, GitHub Actions, GitLab CIKey Responsibilities1. Databricks Platform ManagementWork with Databricks Workspaces, Jobs, Workflows, Unity Catalog, Delta Lake, and MLflow.Optimize clusters, compute resources, and workspace permissions.2. End-to-End ML LifecycleImplement 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 & DevelopmentBuild scalable ML and data pipelines using Python, PySpark, and SQL.Maintain code quality through Git, reviews, and automated testing.4. Cloud & InfrastructureWork across AWS / Azure / GCP for ML operations.Implement IaC using Terraform.Build / manage containerized workloads using Docker / Kubernetes.5. CI / CD & AutomationCreate and optimize CI / CD pipelines for ML workflows.Automate data validation, feature generation, model training & deployment.6. Monitoring & ObservabilityImplement monitoring using Databricks Lakehouse Monitoring for data quality, model drift, and performance.Integrate explainability tools such as SHAP & LIME.7. Feature Engineering & OptimizationWork with Databricks Feature Store.Execute distributed training and hyperparameter tuning using Optuna, Ray Tune, etc.8. Collaboration & DocumentationCollaborate 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 systemsRemote-first work cultureExposure to modern cloud and MLOps technologiesCollaborative and growth-oriented environmentHow to ApplyInterested candidates can share their updated resume at ( ) or apply directly via LinkedIn.