MLOps Engineer

Description :

- Experience : 5 - 10 yrs.

- Locations : Bangalore, Chennai, Mumbai, Pune, Hyderabad.

Key Responsibilities :

- Design, develop, and deploy machine learning models using AWS SageMaker for various business applications.

- Implement end-to-end ML pipelines from data preprocessing to model serving and monitoring.

- Build and maintain automated model training, validation, and deployment workflows.

- Optimize model performance, scalability, and cost-effectiveness in production environments.

- Create interactive ML applications and demos using Gradio for stakeholder demonstrations and user interfaces.

- Develop robust Python applications for data processing, feature engineering, and model inference.

- Build APIs and microservices for model serving and integration with existing systems.

- Implement model versioning, A/B testing frameworks, and continuous integration/deployment practices.

- ML infrastructure on AWS, including SageMaker endpoints, batch transform jobs, and processing jobs.

- Monitor model performance, data drift, and system health in production environments.

- Collaborate with DevOps teams to ensure reliable and scalable ML operations.

- Implement security best practices for ML systems and data handling.

Technical Skills :

- Expert-level proficiency in Python programming with strong software development practices.

- Extensive hands-on experience with AWS SageMaker, including training jobs, endpoints, and pipelines.

- Proven experience with Gradio for building ML application interfaces.

- Strong background in machine learning algorithms, statistical modeling, and deep learning frameworks (PyTorch, TensorFlow, scikit-learn).

- Experience with MLOps practices, model versioning, and deployment strategies.

- Deep understanding of AWS ecosystem (EC2, S3, Lambda, IAM, CloudFormation).

- Experience with containerization technologies (Docker, Kubernetes).

- Knowledge of data engineering tools and workflows (Apache Spark, Airflow, or similar).

- Familiarity with infrastructure as code and CI/CD pipelines.

- Strong experience with version control systems (Git), code review processes, and agile development.

- Excellent problem-solving skills and ability to debug complex distributed systems.

- Experience with data visualization tools and techniques.

- Strong communication skills for presenting technical concepts to diverse audiences.