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

JPMorgan Chase
Plano, USA
Full-time
Senior · 7+ years experience
Onsite
Discovered Yesterday
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Full Job Posting

Job responsibilities:

  • 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.

Required qualifications, capabilities, and skills:

  • 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 considerations.
  • Understanding of responsible AI use in engineering workflows (data sensitivity, safe handling of inputs/outputs, resiliency/security expectations) and ability to guide peers on safe and effective usage.
  • Strong communication and stakeholder management skills; ability to drive alignment, resolve ambiguity, and deliver outcomes across multiple initiatives.

Preferred qualifications, capabilities, and skills:

  • AWS cloud certification(s).
  • AI/ML or GenAI certification(s) (or equivalent practical experience delivering LLM-enabled capabilities).
  • Advanced Java proficiency (primary) and working knowledge of Python.
  • Experience with Docker, Kubernetes, and modern CI/CD practices.
  • Experience with multi-region deployments, disaster recovery, and zero-downtime release patterns.
  • Strong understanding of responsible AI practices, including prompt/agent safety patterns and secure handling of sensitive data.
  • Track record delivering scalable, reliable, and secure products from concept to launch in a regulated environment.
  • Domain familiarity with financial services technology; IVR/CCaaS experience a plus.

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