Lead Software Engineer

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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).