Advanced Model Deployment Specialist

5 days ago

Pune, Maharashtra, India beBeeMachineLearning Full time ₹ 1,04,000 - ₹ 1,30,878
Machine Learning Engineer

Job Summary:

We are seeking a skilled Machine Learning Engineer to oversee the end-to-end lifecycle of machine learning models, from deployment to monitoring and maintenance.

In this role, you will work closely with data scientists, engineers, and development teams to ensure that ML models are effectively integrated into production systems and deliver high performance.

Responsibilities:

  • Deploy and manage machine learning models in production environments, ensuring they are scalable, reliable, and performant.
  • Design and implement CI/CD pipelines for ML models to streamline development and deployment processes.
  • Develop and maintain the infrastructure required for model deployment, including containerization (e.g., Docker), orchestration (e.g., Kubernetes), and cloud services (e.g., AWS).
  • Monitor the performance of deployed models, troubleshoot issues, and perform regular maintenance to ensure models remain accurate and effective.
  • Ensure that model deployment and data handling comply with security and regulatory requirements. Implement best practices for data privacy and protection.
  • Create and maintain documentation for deployment processes, model performance, and system configurations. Provide clear and comprehensive reports to stakeholders.
  • Identify and implement improvements to model performance, deployment processes, and infrastructure efficiency.
  • Participate in regular Scrum events such as Sprint Planning, Sprint Review, and Sprint Retrospective.

Requirements:

  • Bachelor's degree in computer science, Data Science, Statistics, or a related field or a master's degree or higher is preferred.
  • Total 7+ years of industry experience
  • More than 3 years of experience in ML Ops, DevOps, or a related role, with a solid understanding of deploying and managing machine learning models in production environments.
  • Experience with containerization technologies (e.g., Docker) and orchestration platforms (e.g., Kubernetes).
  • Familiarity with cloud services Azure and AWS and their ML offerings
  • Experience with CI/CD tools and practices for automating deployment pipelines (e.g., Azure Pipeline, Azure DevOps).
  • Experience with monitoring and logging tools to track model performance and system health.

Preferred Qualifications:

  • Prior experience in engineering domain would be nice to have
  • Prior experience in working with teams in Scaled Agile Framework (SAFe) is nice to have
  • Knowledge of data engineering and ETL (Extract, Transform, Load) processes.
  • Experience with version control systems (e.g., Git) and collaboration tools.
  • Understanding of machine learning model life cycle management and model versioning.

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