Machine Learning Engineer

Position Overview

We are seeking highly skilled and hands-on MLOps Engineers with 4 to 10 years of experience in designing, deploying, operationalizing, and maintaining Machine Learning solutions in production environments. The ideal candidate will have strong expertise in managing the complete ML lifecycle, building scalable MLOps platforms, automating model deployment pipelines, and ensuring model reliability across cloud and on-premises environments within the Banking and Financial Services domain.

This role requires close collaboration with Data Scientists, ML Engineers, Data Engineers, Infrastructure Teams, DevOps Engineers, Risk & Compliance Teams, and Business Stakeholders to ensure seamless deployment, monitoring, governance, and continuous improvement of predictive models.

Key Responsibilities

ML Model Deployment & Operationalization

• Deploy, manage, and operationalize machine learning models across development, UAT, and production environments.
• Design and implement scalable and reliable model serving architectures for batch, real-time, and near real-time inference.
• Containerize machine learning applications and predictive models using Docker and deploy them using Kubernetes/EKS or equivalent orchestration platforms.
• Ensure smooth migration of models from research environments into enterprise-grade production systems.
• Support model versioning, model promotion workflows, rollback strategies, and release management.

MLOps Platform Development

• Build and maintain robust MLOps frameworks that support the end-to-end machine learning lifecycle.
• Implement model governance, model registry, experiment tracking, reproducibility, and auditing mechanisms using MLflow or similar tools.
• Develop reusable deployment templates, automation frameworks, and CI/CD pipelines for machine learning projects.
• Establish best practices for model packaging, deployment, testing, monitoring, and maintenance.

Data Pipeline Automation

• Design and develop automated data ingestion pipelines using PySpark and Python.
• Build scalable ETL/ELT processes for model training and scoring pipelines.
• Implement data quality validation checks at source and output layers.
• Develop orchestration workflows using Airflow or similar scheduling tools.
• Monitor data movement processes and ensure data integrity across multiple systems.