Description

You will drive the full lifecycle of machine learning solutions, bridging data science model development with production-grade ML Ops Engineering.

Responsibilities

• Develop predictive models using structured/unstructured data across 10+ business lines to drive fraud reduction and operational efficiency.
• Leverage AutoML tools for low-code/no-code model development, documentation automation, and rapid deployment.
• Develop and maintain ML pipelines using tools like MLflow, Kubeflow, or Vertex AI.
• Automate model training, testing, deployment, and monitoring in cloud environments.
• Implement CI/CD workflows for model lifecycle management, including versioning and retraining.
• Monitor model performance using observability tools and ensure compliance with model governance frameworks.

Required Skills

• 10+ years of experience in a relevant engineering capacity.
• Strong proficiency in Python and SQL.
• Experience with ML libraries, including XGBoost, TensorFlow, and PyTorch.
• Hands-on experience with containerization technologies like Docker and Kubernetes.
• Familiarity with data engineering tools such as Airflow and Spark.
• Solid understanding of software engineering principles and DevOps practices.
• Ability to communicate complex technical concepts to non-technical stakeholders.

Key Skills

Education

Any Graduate