Senior Machine Learning Infrastructure Engineer

1 day ago

Hyderabad Secunderabad Telangana, India beBeeMachine Full time ₹ 1,04,000 - ₹ 1,30,878

Machine Learning Operations Expert

This role involves overseeing the deployment, management, and maintenance of machine learning models in production environments.

  • Evaluate and select appropriate cloud infrastructure solutions for machine learning projects, ensuring cost-effectiveness and scalability.
  • Automate the deployment of machine learning models using tools like Docker and Kubernetes.
  • Set up monitoring systems to track model performance and data drift, conduct regular maintenance, and implement updates for production models.
  • Collaborate with cross-functional teams to align project goals, facilitate knowledge sharing, and communicate findings and updates.
  • Design and maintain scalable ML infrastructure, optimizing cloud and on-premise resources for training and inference.
  • Document ML processes, pipelines, and best practices while preparing reports on model performance and resource utilization.
  • Provide training and support for team members on ML Ops tools and methodologies, staying updated on industry trends and emerging technologies.

Requirements

  • 3-6 years of experience in AI/analytics product and solution delivery.
  • Bachelor's/master's degree in an information technology/computer science/engineering or equivalent field experience.
  • Proficiency in frameworks such as TensorFlow, PyTorch, or Scikit-learn.
  • Strong skills in Python and/or R; familiarity with Java, Scala, or Go is a plus.
  • Experience with cloud services such as AWS, Azure, or Google Cloud Platform, particularly in ML services (e.g., AWS SageMaker, Azure ML).
  • CI/CD tools (e.g., Jenkins, GitLab CI), containerization (e.g., Docker), and orchestration (e.g., Kubernetes).
  • Experience with databases (SQL and NoSQL), data pipelines, ETL processes, ML pipeline orchestration (Airflow)
  • Familiarity with monitoring and logging tools such as Prometheus, Grafana, or ELK stack.
  • Proficient in using Git for version control.
  • Strong analytical and troubleshooting abilities to diagnose and resolve issues effectively.
  • Good communication skills for working with cross-functional teams and conveying technical concepts to non-technical stakeholders.
  • Ability to manage multiple projects and prioritize tasks in a fast-paced environment.

Benefits

The ideal candidate will have strong analytical and problem-solving skills, excellent communication skills, and the ability to work effectively in a collaborative environment.

Others

The successful candidate will be responsible for designing, implementing, and maintaining scalable ML infrastructure, optimizing cloud and on-premise resources for training and inference.

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