Live opening · Posted 8 days ago

Lead Software Engineer - Lead Data Architect

JPMorgan Chase · Mumbai, Maharashtra, India
Oracle
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At a glance

The key details from the original listing.

Posted 8 days ago
CompanyJPMorgan Chase
LocationMumbai, Maharashtra, India
SourceOracle
Listed8 days ago

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About the role

Description supplied by the original job listing.

We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible.
As a Lead Software Engineer at JPMorganChase within the Commercial & Investment Bank, you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. As a core technical contributor, you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives.
Job responsibilities
Define and drive end-to-end architecture for complex, distributed, high-throughput systems across the portfolio; shape technology strategy and inform budget and investment prioritization with senior leadership.
Lead design reviews, architecture governance, and technical decisions; establish enterprise-wide patterns, standards, and reference architecture, and represent Engineering & Architecture in senior governance forums (architecture review boards, risk/security committees, regulatory engagements).
Architect cloud-native solutions on AWS with operational rigor across reliability, scalability, security, and cost.
Design and optimize relational and NoSQL data solutions for performance, scale, and reliability—including schema design, indexing, replication, sharding/partitioning, and query optimization.
Build and integrate AI/ML and GenAI into production platforms—LLMs, RAG, embeddings, vector databases, and MLOps for lifecycle management, monitoring, and governance.
Drive adoption of enterprise-authorized AI-assisted engineering practices (code review/refactoring, test acceleration, incident/root-cause analysis) with consistent validation standards (secure coding, peer review, automated testing) and reuse of effective pat

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