Description

You will bridge data science model development and production-grade MLOps engineering, driving the full lifecycle of machine learning solutions.

This role is on-site.

Responsibilities

• Develop predictive models using structured and unstructured data across multiple business lines for fraud reduction and operational efficiency.
• Build and maintain ML pipelines using MLflow, Kubeflow, or Vertex AI to automate training, testing, and deployment.
• Implement CI/CD workflows for model lifecycle management, including versioning, monitoring, and retraining in cloud environments.
• Monitor model performance using observability tools and ensure compliance with governance frameworks like MRM and explainability.
• Collaborate with engineering teams to provision containerized environments and support model scoring via low-latency APIs.

Required Skills

• 10+ years of professional experience in software engineering or MLOps.
• Strong proficiency in Python and SQL.
• Experience with ML libraries including SciKit-learn, XGBoost, TensorFlow, and PyTorch.
• Expertise in containerization using Docker and Kubernetes.
• Hands-on 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.

Preferred Skills

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

Key Skills

Education

Any Graduate