- 2-4 years of experience in ML Engineering/MLOps/DevOps. Experience in NBFCs, financial services, viz., commercial banking or investment banking, is desired.

- Familiarity with agile methodologies.

- Strong Python skills, essential for building and automating ML/data workflows.

- Familiarity with CI/CD pipelines, infrastructure-as-code (IaC), and MLOps/DevOps best practices (monitoring, drift detection, retraining).

- Knowledge of ML model versioning, deployment, and monitoring (tools like MLflow, Kubeflow, etc. ) is highly desirable.

- Ability to write SQL and NOSQL queries across extensive data volumes.

- Design, develop, and maintain end-to-end ML pipelines, from data ingestion to model deployment and monitoring Automate ML workflows including training, validation, deployment, retraining, and performance monitoring Implement CI/CD pipelines for machine learning models and data pipelines

- Build and manage Infrastructure as Code (IaC) for scalable ML platforms

- Deploy and monitor ML models in production environments ensuring reliability, scalability, and performance

- Implement model versioning, experiment tracking, and monitoring using tools such as MLflow, Kubeflow, or similar Monitor model drift, data drift, and trigger automated retraining processes

- Collaborate with Data Science teams to productionize models efficiently

- Work with large-scale datasets and write optimized SQL and NoSQL queries Ensure compliance with security, governance, and regulatory standards, especially for financial services Participate in Agile ceremonies and contribute to continuous improvement initiatives

Required Skills & Qualifications :

- 2-4 years of hands-on experience in ML Engineering, MLOps, or DevOps

- Strong proficiency in Python for ML and automation workflows

- Solid understanding of CI/CD pipelines, DevOps, and MLOps best practices

- Experience with model deployment, monitoring, and lifecycle management Familiarity with Agile methodologies and collaborative development practices

- Strong working knowledge of SQL and NoSQL databases and large-scale data handling Good understanding of cloud-based ML platforms and containerized environments

Preferred / Good-to-Have Skills :

- Prior experience in NBFCs, FinTech, Commercial Banking, or Investment Banking Hands-on experience with MLflow, Kubeflow, Airflow, or similar tools

- Exposure to Docker, Kubernetes, or cloud-native deployments Understanding of data governance, compliance, and regulatory requirements Experience in monitoring, logging, and alerting for ML systems

Educational Qualifications :

- Bachelors or Masters degree in Computer Science, Data Science, Engineering, or a related field

Why Join Us :

- Work on real-world financial services ML problems Opportunity to build production-grade ML systems at scale Collaborative, fast-paced environment with strong learning opportunities Exposure to modern MLOps tools and cloud technologies