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.
Job 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.
Required Qualifications, Capabilities, and Skills
- 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.
Preferred Qualifications, Capabilities, and Skills
- 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).