Lead Software Engineering - Java​/Python - AI

Job Description

We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible.

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 JPMorgan

Chase within the Corporate Sector Technology, 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.

• Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team.
• Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation.
• Architect and implement resilient, highly scalable, fault-tolerant, low-latency services and drive target-state architecture.
• Design and deploy services that integrate with enterprise systems; ensure functional, performance, scalability, security, governance, and auditability requirements are met.
• Lead and mentor the development team in a high-pressured delivery environment; manage multiple deliverables across business groups and strengthen stakeholder relationships.
• Collaborate with LOB users, SMEs, architects, DBAs, and system administrators to design solutions, manage enhancements, and resolve issues.
• Build and mature capabilities that execute ML pipelines for fraud detection and risk assessment; support modeling teams in implementation and tooling.
• Production Alize models built by data scientists, including validation readiness and quality controls prior to live usage.
• Design and own reusable ML platform components (e.g., feature-store patterns, delivery pipelines) and establish monitoring/alerting for performance, scalability, availability, and reliability.
• Build agentic AI services to automate and enhance engineering and model-ops workflows (tool-using agents, orchestration, state management, and audit-ready traceability).
• Define and implement guardrails and evaluation approaches for agentic AI in production (quality, safety, latency, and cost).

• Formal training or certification on software engineering concepts and 5+ years applied experience
• 10+ years of recent hands-on software development experience in large-scale distributed systems, primarily Java/J2EE and modern Java/Spring Boot microservices.
• 3+ years of experience developing in Linux environments.
• Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security.
• Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices
• Strong experience with REST APIs and service-oriented / microservices architecture.
• Strong Kubernetes orchestration experience (building, deploying, and operating production services).
• Messaging expertise with Kafka, MQ, or similar platforms.
• Experience with backend infrastructure patterns (e.g., load balancing, autoscaling).
• Experience with log analytics / observability tools (e.g., ELK, Splunk).
• AI/ML platform exposure (MLOps, feature engineering, model…