ML Ops Engineer

3 weeks ago

New Delhi, India Wenger & Watson Full time

Experience - 5 - 10 yrsLocations - Bangalore, Chennai, Mumbai, Pune, HyderabadKey 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 handlingTechnical 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

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