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Strong Tier-1 brand, metro location, and broad ML/Cloud skillset drive high competition.
Core MLOps, cloud, and data engineering skills transfer across industries but require domain experience.
Multiple mandatory technical domains (MLOps, cloud, data, production engineering) imply strict technical filters.
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Lead design and delivery of cloud-native data engineering and AIML software solutions, ensuring secure, stable, and scalable production code.
Drive adoption and governance of AI-assisted engineering practices across teams to improve code quality, delivery speed, and operational outcomes with measurable standards.
Build and maintain engineering stacks for data and AIML products, including data engineering, backend, Cloud infra DevOps, and MLOps with architecture accountability.
Proficient coding skills in Python and experience in application development, system design, testing, and operational stability.
Demonstrated leadership in enterprise-authorized AI-assisted software development tools and strong understanding of responsible AI use including security and data sensitivity.
Experience with Cloud services, Infrastructure as Code, containerized application development, and modern big data/data engineering technologies.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in architecting microservices, distributed systems, and data-intensive applications in commercial/enterprise environments, preferably financial sector.
Skilled at integrating AI/ML engineering with Cloud-native production-scale environments and familiar with Cloud Data engineering services and MLOps.
Able to convey complex design choices and collaborate effectively with diverse technical and non-technical stakeholders.