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

JPMorganChase•Plano, TX

About The Position

As a Senior Lead Software Engineer in JPMorganChase’s Consumer & Community Banking, OPS Tech organization, you will help an agile team build and deliver trusted technology products that are secure, stable, and scalable. You will use deep technical expertise and problem-solving skills to address challenges across multiple technologies and applications, while driving significant business impact. You will lead complex full-stack initiatives from concept through production, set technical direction and architecture, and deliver cloud-native microservices and APIs. The role also leads development of AI-driven capabilities, including LLM-based services and integrations into business workflows, and promotes approved AI-assisted engineering practices. You will establish engineering and operational standards, own reliability outcomes, strengthen observability and delivery practices, mentor engineers, and coordinate priorities and dependencies with stakeholders in an Agile/Scrum environment.

Requirements

Formal training or certification in software engineering concepts, plus 5+ years of 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 production operational-stability skills, including demonstrated ownership of on-call readiness and incident outcomes.
Expertise designing and building microservices and RESTful APIs, 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, including scalability, resiliency, concurrency, idempotency, failure modes, and performance tuning, and ability to guide design reviews.
Ability to influence product design and technical direction through pragmatic architectures, tradeoffs, and standards aligned with firm SDLC, security, and controls expectations.
Hands-on experience using enterprise-authorized AI-assisted software development tools in the work environment, including evaluating, validating, and refining AI-generated outputs for correctness, performance, and security.
Experience leading effective team use of approved AI-assisted development practices for coding, code review, test acceleration, and troubleshooting, including establishing team expectations and reusable validation patterns.
Hands-on experience with LLMs and generative AI use cases such as RAG, summarization or extraction, and workflow assistants, including evaluation and monitoring considerations.
Understanding of responsible AI use in engineering workflows, including data sensitivity, safe handling of inputs and outputs, and resiliency and security expectations, with the ability to guide peers.
Strong communication and stakeholder-management skills, including the ability to drive alignment, resolve ambiguity, and deliver outcomes across multiple initiatives.

Nice To Haves

AWS cloud certification(s).
AI/ML or GenAI certification(s), or equivalent practical experience delivering LLM-enabled capabilities.
Advanced Java proficiency 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 and 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.
Familiarity with financial-services technology; IVR/CCaaS experience is a plus.

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 or domain, define target architecture, make roadmap-level technical decisions, and align designs with business priorities and constraints.
Architect and deliver secure, scalable, resilient, performant, and production-ready cloud-native microservices and APIs.
Lead development of AI-driven capabilities, including LLM-based services, orchestration, and integrations into business workflows, and establish reusable patterns across squads.
Drive team adoption of enterprise-authorized AI-assisted engineering tools and practices, setting validation standards for secure coding, peer review, automated testing, and reuse.
Establish and enforce engineering standards for coding, APIs, testing, dependency management, documentation, and operational readiness.
Own reliability outcomes for key services by defining SLIs and SLOs, leading incident response and root-cause analysis or postmortems, and improving stability, latency, and cost.
Lead observability and operational automation strategy, including logs, metrics, tracing, alert quality, runbooks, and auto-remediation, to reduce recurring issues and improve MTTR.
Improve CI/CD maturity and safe delivery through quality gates, canary or blue-green releases, feature flags, and rollback strategies.
Mentor and develop engineers through technical coaching, architecture reviews, and feedback, fostering technical excellence and continuous improvement.
Communicate status, risks, and tradeoffs across stakeholders, and manage priorities and dependencies across multiple initiatives in an Agile/Scrum environment.