MLE/MLOps, OOPs Python, Databricks, Azure

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 Py Spark 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)

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, Tensor Flow, Py Torch)
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

Education: MCA,MSc,MTech,Bachelor of Engineering,BCA,BSc,BTech

Preferred skills: Technology->AI-Data science->Databricks Machine Learning,Technology->AI-Data science->PYTHON,Technology->Data Engineering->Databricks,Technology->Cloud Platform->Azure Networking Services-> Azure NAT Gateway