Machine Learning Engineer / MLOps Engineer

MLE/MLOps, OOPs Python, Databricks, Azure

Required Skills & Qualifications Core Skills 3–5 years of experience in Machine Learning / MLOps Strong proficiency in Python with OOP concepts (mandatory) Hands-on experience with Databricks & PySpark Solid experience with Azure cloud ecosystem Technical Skills Experience with ML frameworks (Scikit-learn, TensorFlow, PyTorch) Hands-on with MLflow (experiment tracking & model registry) Knowledge of CI/CD tools (Azure DevOps, Jenkins, GitHub Actions) Strong understanding of data structures, algorithms, and system design basics Experience with REST APIs and microservices Preferred Skills Exposure to feature stores and model monitoring tools Knowledge of Docker & Kubernetes Familiarity with Delta Lake, data lakes, and warehouse architectures Experience with streaming (Kafka/Event Hub) Understanding of data governance and security best practices Key Responsibilities Machine Learning Engineering Develop, train, evaluate, and deploy machine learning models at scale Implement end-to-end ML pipelines from data ingestion to model serving Work on model optimization, validation, and performance monitoring Apply best practices for feature engineering and model lifecycle management MLOps & Deployment Build and maintain MLOps pipelines for CI/CD/CT (Continuous Training) Automate model deployment, versioning, and monitoring Implement experiment tracking and model registry (MLflow preferred) Ensure model reproducibility, scalability, and governance Python (OOPs) Development Develop modular, reusable, and scalable code using object-oriented Python Build robust backend services and ML utilities Write clean, testable, and well-documented code Databricks Develop and optimize workflows on Azure Databricks Work with PySpark for data processing and feature engineering Manage notebooks, jobs, clusters, and Delta Lake pipelines Optimize Spark jobs for performance and cost Azure Cloud Work with Azure services like Azure ML, Data Factory, Blob Storage, ADLS, Key Vault Deploy models and pipelines using Azure DevOps / CI-CD pipelines Implement secure, scalable, and cost-efficient cloud architectures Data Engineering & Integration Build and maintain data pipelines for ML workflows Integrate models with APIs and downstream applications Work with large datasets (structured & unstructured)