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Tier-1 brand and Bangalore raise competition, but senior ML/MLOps specialization limits applicant density.
Strong ML, data engineering and MLOps requirements are transferable yet moderately industry-sensitive.
Multiple mandatory skills across ML, data, cloud, and production engineering create high filtering.
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Lead design and delivery of cloud-native data, backend, and AIML engineering solutions supporting commercial and investment banking business functions.
Drive adoption and governance of AI-assisted software engineering practices to improve code quality, delivery speed, and operational outcomes.
Build and maintain secure, production-scale microservices, distributed systems, and data engineering platforms with a focus on automation, DevOps, and MLOps.
Proficient coding experience in Python and modern programming languages; hands-on system design and application development experience.
Demonstrated leadership in using enterprise AI-assisted software development tools with understanding of responsible AI use and security considerations.
Experience with cloud services, Infrastructure as Code, containerized application development, and big data technologies; practical knowledge of cloud data engineering and MLOps.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in architecting and developing microservices and distributed systems at production scale within commercial or financial sectors.
Skilled in integrating AI/ML engineering workflows into software development lifecycle, with a strong focus on secure, compliant, and resilient engineering.
Familiar with cloud-native data engineering stacks including ETL, AWS services (Glue, S3, Athena), Kubernetes, Docker, and MLOps tooling.