ML Ops Engineer

3 weeks ago

Bengaluru Karnataka India, Karnataka Wenger & Watson Full time

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

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