- Build and optimize data pipelines for point-in-time historical data access and feature backfilling.

- Containerize and deploy ML models and services using Docker and Seldon on Kubernetes clusters.

- Integrate services with Clients GCP infrastructure and internal APIs for smooth simulation workflows.

- Collaborate with cross-functional teams including data scientists, ML engineers, and QA.

- Contribute to CI/CD pipelines and DevOps practices for automated testing, deployment, and monitoring.

- Write clean, maintainable, and well-documented code following best practices.

- Participate in code reviews, design discussions, and architecture decisions.

- Troubleshoot and optimize simulation jobs and platform services for performance and reliability.

Required Qualifications :

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

- Proficiency in Java and Spring Boot; experience with Graph DB (Gremlin) is mandatory.

- Solid understanding of APIs, microservices, and distributed system design.

- Experience with Docker and Seldon for model deployment.

- Hands-on experience with Kubernetes (preferably on GCP).

- Knowledge of CI/CD, DevOps tools, and Git for version control.

- Strong analytical, debugging, and communication skills.

Preferred Qualifications :

- Experience with feature engineering, data backfilling, and point-in-time data access.

- Exposure to large-scale ML or financial platforms.

- Familiarity with Jupyter Notebooks and AI SDKs.

- Understanding of MLOps best practices model versioning, monitoring, and automation.

- Experience with big data tools and scalable data processing frameworks.