JOB DESCRIPTION
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 patterns.
- Develop senior technical talent and partner across functions—lead hiring, calibration, and succession for principal/staff engineers, grow the architecture community of practice, and align with Product, Risk, Security, and Infrastructure leaders on outcomes, regulatory expectations, and risk posture.
- Own non-functional requirements and emerging-tech evaluation—drive performance, resiliency, observability, security, compliance, and cost; define and enforce measurable SLOs/SLAs; lead proof-of-concepts and convert learnings into enterprise adoption plans
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Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team.
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Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation.
Required qualifications, capabilities, and skills
Preferred qualifications, capabilities, and skills
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Exposure in Financial Services or other highly regulated industries, with practical understanding of auditability, change control, and security expectations.
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Contributions to open-source projects, patents, or published technical work that demonstrates technical depth and community credibility.
- Experience with multi-region, active-active architectures, disaster recovery design, and zero-downtime migrations.
- Familiarity with security frameworks, threat modeling, secure SDLC, and zero-trust architecture.
- Experience leading platform modernization, large-scale cloud migrations, or modernization of legacy monoliths into service-based architectures.
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