Job Title: Data Scientist Azure ML / MLOps

Experience: 5–8 Years

Location: Hyderabad / Pune / Noida

Employment Type: Full-Time

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 settings.

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.
Solid 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 robust 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.

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