Description

You will build and productionize machine learning models and data engineering pipelines.

This role is on-site.

Responsibilities

• Build and maintain production-grade ML pipelines.
• Productionize ML models using MLOps practices and lifecycle management.
• Drive low-level technical implementation and high-level architecture evolution.
• Collaborate with multi-functional teams, principals, and architects across geographies.
• Develop and manage data engineering pipelines.

Required Skills

• 5+ years of experience in machine learning and data science.
• Proficiency in Python and frameworks such as TensorFlow, Keras, or PyTorch.
• Hands-on experience with MLOps tools including MLFlow, Kubeflow, Weights & Biases, AWS SageMaker, Vertex AI, DVC, Airflow, or Prefect.
• Experience with CI/CD pipelines, Git, Docker, and Jenkins.
• Knowledge of cloud platforms including AWS, GCP, or Azure.
• Experience with Kubernetes and Kafka.
• Background in statistical modeling, machine learning, data mining, and unstructured data analytics.
• Experience with cloud build, deployment, and orchestration tools.
• Any Graduate degree.

Key Skills

Education

Any Graduate