Description

You will build and maintain the infrastructure for deploying, managing, and monitoring machine learning models in production.

Responsibilities

• Build and maintain end-to-end machine learning pipelines covering data preprocessing, training, validation, and deployment.
• Develop automation frameworks to streamline model management and optimize workflow scalability.
• Review data science code, refactor for quality, and oversee containerization and version control.
• Monitor deployed model performance and health to proactively troubleshoot bottlenecks.
• Collaborate with data engineers to ensure efficient data ingestion and storage for ML workflows.

Required Skills

• 5 to 8 years of experience in end-to-end machine learning pipelines.
• Proficiency in Python, Kubernetes, and Docker.
• Cloud experience with AWS or GCP.
• Strong programming skills in Python, Go, or Spark.
• Experience with scikit-learn, Keras, PyTorch, or TensorFlow.
• In-depth understanding of Kubeflow, MLFlow, and Nvidia-toolkit.
• Knowledge of Linux, configuration management, and CI/CD pipelines.
• Solid understanding of software engineering principles, version control, and testing.
• Familiarity with Infrastructure as Code (IaC) and DevOps practices.

Preferred Skills

• Experience in AI/ML based projects.

Key Skills

Education

ANY GRADUATE