AI Model Deployment Expert

1 day ago

Pune, Maharashtra, India beBeeDeployment Full time ₹ 15,00,000 - ₹ 28,00,000

Job Title: AI Model Deployment Expert

 

The role of AI Model Deployment Expert involves overseeing the lifecycle of machine learning models from deployment to monitoring and maintenance. Working closely with data scientists, machine learning engineers, and development teams ensures that ML models are effectively integrated into production systems and deliver high performance.

 

Key Responsibilities Include:

  • Deploying and managing machine learning models in production environments to ensure scalability, reliability, and performance.
  • Designing and implementing CI/CD pipelines for ML models to streamline development and deployment processes.
  • Developing and maintaining infrastructure required for model deployment, including containerization (e.g., Docker), orchestration (e.g., Kubernetes), and cloud services (e.g., AWS, Google Cloud, Azure).
  • Monitoring the performance of deployed models, troubleshooting issues, and performing regular maintenance to ensure models remain accurate and effective.
  • Ensuring that model deployment and data handling comply with security and regulatory requirements by implementing best practices for data privacy and protection.
  • Creating and maintaining documentation for deployment processes, model performance, and system configurations. Providing clear and comprehensive reports to stakeholders.
  • Identifying and implementing improvements to model performance, deployment processes, and infrastructure efficiency.

 

Requirements:

The ideal candidate will have a Bachelor's degree in computer science, Data Science, Statistics, or a related field, and at least 7 years of industry experience. A minimum of 3 years of experience in ML Ops, DevOps, or a related role is also required. Strong understanding of deploying and managing machine learning models in production environments is essential.

 

Preferred Qualifications:

Prior experience in engineering domain is preferred. Experience working with teams in Scaled Agile Framework (SAFe) is also beneficial. Knowledge of data engineering and ETL processes, as well as experience with version control systems (e.g., Git) and collaboration tools, is an advantage.

 

Machine learning model life cycle management and model versioning skills are highly valued in this role.

 

Benefits:

This role offers a unique opportunity to work on cutting-edge AI projects and contribute to the growth of the organization.

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