Data Scientist Azure ML MLOps

Job Title: Data Scientist Azure ML / MLOps

Experience: 5–8 Years

Location: Hyderabad / Pune / Noida

Employment Type: Full-Time

Job Description

We are looking for an experienced Data Scientist with strong hands-on expertise in Machine Learning, Python, Azure Machine Learning and MLOps. The candidate will be responsible for developing, deploying, monitoring and maintaining machine learning models in production environments.

Key Responsibilities

• Develop, train, evaluate and optimize machine learning and statistical models using Python.
• Build and deploy ML models using Azure Machine Learning Studio and related Azure services.
• Develop automated ML pipelines for model training, deployment, testing and monitoring.
• Implement MLOps practices, including model versioning, experiment tracking and CI/CD automation.
• Monitor model performance, identify data/model drift and implement retraining strategies.
• Collaborate with data engineering and business teams to prepare datasets, engineer features and deliver scalable ML solutions.
• Optimize model performance, reliability and deployment efficiency in production environments.

Mandatory Skills

• Python programming and machine learning.
• Hands-on experience with Azure Machine Learning / Azure ML Studio.
• Strong understanding of model development, evaluation and deployment.
• Experience implementing MLOps pipelines and CI/CD for machine learning.
• Model monitoring, performance evaluation and retraining concepts.
• Experience with MLflow or similar tools for experiment tracking and model management.

Good-to-Have Skills

• Azure Data Factory, Azure Databricks and Azure Data Lake Storage.
• Docker and Kubernetes.
• Automated model retraining and model-drift detection.
• Experience with NLP, deep learning or Generative AI.
• Experience working with large datasets and production ML systems.

Candidate Profile

Candidates should have strong hands-on experience in both machine learning and production deployment. Preference will be given to candidates who have implemented end-to-end ML solutions using Azure ML and established MLOps practices.