Description

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

Responsibilities

• Develop predictive models using structured/unstructured data across 10+ business lines for fraud reduction, operational efficiency, and customer insights.
• 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 (GCP, AWS, Azure).
• Implement CI/CD workflows for model lifecycle management, including versioning, monitoring, and retraining.
• Monitor model performance using observability tools and ensure compliance with model governance frameworks (MRM, documentation, explainability).
• Collaborate with engineering teams to provision containerized environments and support model scoring via low-latency APIs.

Required Skills

• 10+ years of professional experience.
• Strong proficiency in Python and SQL.
• Experience with ML libraries including SciKit-learn, XGBoost, TensorFlow, and PyTorch.
• Expertise in containerization using Docker and Kubernetes.
• Experience with cloud platforms, specifically GCP and AWS.
• Solid understanding of software engineering principles and DevOps practices.
• Familiarity with data engineering tools such as Airflow or Spark.
• Ability to communicate complex technical concepts to non-technical stakeholders.

Key Skills

Education

Any Graduate