ML Ops Engineer

As a Machine Learning Operations (ML Ops) Engineer, you will play a pivotal role in bridging the gap between data science and IT operations. Your primary responsibility will be to streamline and operationalize the end-to-end machine learning lifecycle, ensuring the smooth deployment, monitoring, and maintenance of machine learning models in production environments.

Key Responsibilities:

  • Deployment and Integration
    • Deploy machine learning models into production environments, collaborating with data scientists and software engineers to integrate models into existing systems.
    • Develop and maintain deployment pipelines for machine learning models, utilizing tools like Docker, Kubernetes, and other containerization technologies.
    • Utilize Amazon SageMaker for seamless model deployment and management, taking advantage of its scalable infrastructure and built-in tools for model training and hosting.
    • Develop and maintain deployment pipelines for machine learning models, integrating with MLflow for experiment tracking and model versioning.
  • Automation and Orchestration:
    • Implement automation scripts and tools to facilitate seamless model deployment, monitoring, and scaling.
    • Orchestrate the workflow between data science, development, and operations teams to optimize the end-to-end ML lifecycle.
  • Infrastructure Management:
    • Manage the infrastructure required for machine learning model deployment, including cloud resources and on-premises servers.
    • Optimize infrastructure for scalability, performance, and cost-effectiveness.
    • Utilize Data Version Control (DVC) to manage and version machine learning data, ensuring reproducibility and traceability.
    • Utilize AWS services, including SageMaker, for efficient model hosting and management.
  • Monitoring and Maintenance:
    • Establish monitoring systems to track model performance, resource utilization, and other relevant metrics.
    • Monitor and address model drift, implementing strategies for detecting and mitigating changes in data patterns that impact model accuracy.
    • Collaborate with data scientists to develop strategies for model retraining and versioning to address model drift.
    • Leverage MLflow for experiment tracking and model versioning, ensuring reproducibility and traceability.
  • Security and Compliance:
    • Implement security best practices for machine learning systems, ensuring the confidentiality and integrity of data and models.
    • Ensure compliance with relevant regulations and standards in the deployment and maintenance of machine learning models.
  • Collaboration and Communication:
    • Work closely with cross-functional teams, including data scientists, software engineers, and operations, to foster a collaborative and efficient working environment.
    • Clearly communicate technical concepts and requirements to non-technical stakeholders.


Bachelor’s or master’s degree in computer science, Machine Learning, or a related field.

  • 3-5 years of proven experience in deploying and managing machine learning models in production environments.
  • Strong proficiency in programming languages such as Python, and experience with relevant frameworks (e.g., TensorFlow, PyTorch).
  • Familiarity with containerization tools (Docker, Kubernetes) and cloud platforms (AWS, Azure).
  • Solid understanding of DevOps principles and practices.
  • Experience with version control systems (Git) and continuous integration/continuous deployment (CI/CD) pipelines.
  • Knowledge of security best practices in machine learning deployments.
  • Experience in addressing and mitigating model drift.
  • Familiarity with Data Version Control (DVC) for managing and versioning machine learning data.
  • Proficiency in Amazon SageMaker and MLflow for model deployment, management, and experiment tracking.
  • Excellent communication and collaboration skills.

Job Type: Full Time
Job Location: Hyderabad
Work Type: Hybrid

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