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 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.
Job responsibilities:
- 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).
Required qualifications, capabilities, and skills:
- 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 hosting/operationalization; AWS and/or hybrid on-prem + cloud).
Preferred qualifications, capabilities, and skills:
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Strong communication skills and proven ability to influence across senior technology and business stakeholders.
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Strong SDLC knowledge and agile ways of working, including CI/CD, application resiliency, security, testing, and operational stability.
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Agentic AI experience preferred: building and operating LLM-driven agents with tool integration, monitoring/telemetry, and governance/audit considerations.
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Experience with NoSQL databases such as Cassandra (preferred).