Lead Software Engineer

JPMorgan Chase & Co.•Columbus, OH

About The Position

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 Chief Technology office - Intelligent Solutions Product Line team, 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.

Requirements

Formal training or certification on software engineering concepts and 5+ years applied experience
8+ years of hands-on software engineering experience delivering system design, application development, testing strategies, and production operations/stability.
Advanced proficiency in Java (and/or strong proficiency in Python), including strong knowledge of software engineering best practices, design patterns, performance, and secure coding.
Demonstrated experience effectively using enterprise-authorized AI assistant tools in engineering workflows (e.g., coding support, code review, test acceleration, troubleshooting), with appropriate validation and controls.
Experience with platform engineering and enabling reusable engineering capabilities at scale (developer experience, automation platforms, shared services).
Experience implementing AI/ML-enabled applications at the code level (e.g., LLMs, Generative AI, NLP), including integration patterns such as RAG, evaluation/quality approaches, and safe deployment practices.
Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency, privacy, and security expectations.
Experience building and improving CI/CD pipelines and automation across the SDLC, reducing manual steps and improving repeatability and auditability.
Cloud-native engineering experience (preferably AWS) including containerization, observability, and operational readiness.
Prior experience leading/mentoring engineers as a hands-on practitioner delivering production-grade solutions.

Nice To Haves

Experience integrating vendor platforms within large enterprises (architecture, controls, upgrades, operational support, and extensibility).
Familiarity with agentic workflow concepts and frameworks (examples may include LangChain/LangGraph-style patterns where enterprise-approved), including orchestration, tool-use patterns, and evaluation/guardrails.
Knowledge of financial services technology environments, operating models, and control expectations.
Strong practical experience with modern cloud-native architectures (microservices, event-driven patterns, infrastructure as code, resilience patterns).

Responsibilities

Design and deliver creative software solutions through architecture, development, and technical troubleshooting, applying non-routine thinking to decompose complex problems and build durable solutions.
Architect, build, and operate secure, high-quality production systems using Java, and AI code generation assistant tools aligned to CTO standards for resiliency, availability, and risk controls.
Drive adoption of enterprise-authorized AI-assisted engineering practices (e.g., AI-assisted code review/refactoring, test acceleration, troubleshooting support), establishing validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across teams.
Partner with product and engineering leadership to shape technical vision and execute roadmaps across ISPL capabilities (process/decisioning, workflow/robotics automation, data transformation, and business intelligence).
Implement and continuously improve automation-first engineering practices including CI/CD, environment management, and release controls to reduce manual interventions for both vendor-integrated and custom-built solutions.
Integrate AI/ML capabilities into software systems where appropriate (e.g., LLM-powered experiences, retrieval-augmented patterns, NLP workflows), while ensuring secure handling of sensitive data and outputs.
Design experiments, implement algorithms, evaluate results, and produce scalable and observable AI/ML components and services (including model/service performance, latency, and reliability).
Identify recurring production issues and drive automation/remediation to improve system reliability, reduce toil, and raise operational maturity (instrumentation, alerting, incident response, and root-cause elimination).
Mentor and coach engineers and AI practitioners within a global organization, reinforcing engineering excellence, ownership, and accountability.