Responsibilities
1. Drive analytic projects for all pillars of the bank’s treasury and markets department, such as bond analytics, news generation FX trading signals, product recommendation and customer hyper-personalization.
2. Implement and train AI/ ML models and optimize algorithm efficiency (GPU distributed computing, concurrent programming)
3. Write and refactor code into reusable libraries/ APIs/ tools, deploy machine learning ecosystems/ pipelines and perform sub-system integration as required.
4. Integrate solutions into enterprise MLOps ecosystem and automate CI/CD pipelines for model lifecycle maintenance and monitoring. This can include monitoring for data drift, triggering model retraining and setting up rollbacks.
5. Run experiments to test the performance of deployed models, and identifies and resolves bugs that arise in the process
Required Experience
1. Bachelor/ masters in a quantitative discipline, such as Mathematics/Statistics, Business Analytics, Computer Science, Engineering, or equivalent experience.
2. Experience building scalable machine learning system architectures (microservice, distributed, etc.) and big-data pipelines in production.
3. Familiar with Linux OS, Openshift, Kubernetes for container-based deployment.
4. Demonstrable understanding and application of varied AI/ML concepts and models, their mathematical underpinnings, and trade-offs
5. Excellent software skills (Python, SQL variants) and knowledge in design patterns, code optimization, object-oriented programming.
6. Experience in defining the processes, standards, frameworks, prototypes and toolsets in support of AI and ML development, monitoring, testing and operationalization.
7. Familiar with software development best practices and tools (Agile, Jira, Jenkins, Git)
