Senior Lead Software Engineer - Java​/Python​/AWS​/AI​/LLM

Be an integral part of an agile team that's constantly pushing the envelope to enhance, build, and deliver top-notch technology products.

As a Senior Lead Software Engineer at JPMorgan

Chase within the Consumer & Community Banking, OPS Tech, 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. Drive significant business impact through your capabilities and contributions, and apply deep technical expertise and problem-solving methodologies to tackle a diverse array of challenges that span multiple technologies and applications.

• Lead end-to-end delivery of complex full-stack initiatives from concept through production launch, balancing speed, quality, risk, and maintainability.
• Set technical direction for the team/domain: define target architecture, drive roadmap-level technical decisions, and align designs to business priorities and constraints.
• Architect and deliver cloud-native microservices and APIs with strong security controls, scalability, resilience, and performance; ensure designs are operationally production-ready.
• Lead development of AI-driven capabilities, including LLM-based services, orchestration, and integrations into business workflows; establish patterns that are reusable across squads.
• Drive team adoption of enterprise-authorized AI-assisted engineering practices (e.g., CoPilot, Claude) to improve code quality, delivery speed, and operational outcomes; set consistent validation standards (secure coding, peer review, automated testing) and promote reuse.
• Establish and enforce engineering standards across the team (coding standards, API guidelines, testing strategy, dependency management, documentation expectations, and operational readiness).
• Own reliability outcomes for key services: define SLIs/SLOs, lead incident response and RCA/postmortems, and drive continuous improvements to stability, latency, and cost.
• Lead observability and operational automation strategy (logs/metrics/tracing, alert quality, runbooks, auto-remediation) to reduce recurring issues and improve MTTR.
• Drive CI/CD maturity and safe delivery practices (quality gates, canary/blue-green, feature flags, rollback strategy), enabling predictable releases and zero/low downtime changes.
• Mentor and grow engineers (senior and junior) through technical coaching, architecture reviews, and feedback; cultivate a culture of technical excellence and continuous improvement.
• Communicate status, risks, and tradeoffs clearly across stakeholders; manage priorities and dependencies across multiple initiatives while operating effectively in Agile/Scrum.

• Formal training or certification on software engineering concepts and 5+ years applied experience in addition to 7+ years of applied experience building production systems, including leading delivery of complex initiatives.
• Strong system design, application development, and operational stability skills in production environments, with demonstrated ownership of on-call readiness and incident outcomes.
• Expertise designing and building microservices and RESTful APIs, with strong knowledge of data technologies and integration patterns, and ability to set standards used by others.
• Hands-on experience building cloud-native systems on at least one major cloud platform (AWS, GCP, or Azure), including production deployment and operationalization.
• Experience with distributed systems concepts (scalability, resiliency, concurrency, idempotency, failure modes, and performance tuning) and ability to guide design reviews.
• Proven ability to influence product design and technical direction by proposing pragmatic architectures, tradeoffs, and standards aligned to firm SDLC, security, and controls expectations.
• Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment, with demonstrated ability to evaluate, validate, and refine AI-generated outputs for correctness, performance, and security.
• Demonstrated experience leading effective team use of approved AI-assisted development practices (coding, code review, test acceleration, troubleshooting), including establishing team expectations and reusable validation patterns.
• Hands-on experience with Large Language Models (LLMs) and generative AI use cases (e.g., RAG, summarization/extraction, workflow assistants), including evaluation and monitoring…