Description

You will bridge data science model development with production-grade ML Ops engineering, driving the full lifecycle of ML solutions.

This role is hybrid.

Responsibilities

• Develop predictive models using structured and unstructured data to reduce fraud and improve operational efficiency.
• Build and maintain ML pipelines using MLflow, Kubeflow, or Vertex AI.
• Automate model training, testing, deployment, and monitoring in cloud environments.
• Implement CI/CD workflows for model versioning and retraining.
• Monitor model performance with observability tools and ensure governance compliance.

Required Skills

• 10+ years of experience in an engineering capacity.
• Strong proficiency in Python and SQL.
• Hands-on experience with XGBoost, TensorFlow, and PyTorch.
• Experience with Docker and Kubernetes for containerization.
• Familiarity with Airflow and Spark for data engineering.
• Solid understanding of software engineering principles and DevOps practices.

Preferred Skills

• Experience leveraging AutoML tools for low-code/no-code model development and documentation automation.
• Ability to communicate complex technical concepts to non-technical stakeholders.

Key Skills

Education

Any Graduate