Lead Software Engineering - Java/Python - AI

JPMorgan Chase & Co.; Plano (TX); On-site; USD 180,000 - 250,000; Full time

Job summary

JPMorganChase in Plano, Texas seeks a Lead Software Engineer to drive AI-assisted engineering practices and deliver high-quality, scalable Java/Spring Boot microservices. You will architect resilient services, mentor developers, and collaborate with architects, DBAs, and SMEs to build secure, auditable systems.

The role emphasizes production-grade ML pipelines, agentic AI capabilities, and robust DevOps with CI/CD, security, and operations focus in a fast-paced enterprise setting.

Qualifications

• Formal training or certification on software engineering concepts and 5+ years applied experience
• 5+ years of software engineering in large-scale distributed systems (Java/J2EE, Spring Boot)
• 3+ years of Linux development experience
• Experience leading AI-assisted software development tools and validating AI outputs for correctness, performance, and security
• Strong understanding of responsible AI use in engineering workflows and secure data handling
• Strong REST APIs and microservices architecture experience
• Kubernetes orchestration experience (production services)
• Kafka or similar messaging platforms
• Experience with log analytics/observability tools (ELK, Splunk)
• AI/ML platform exposure (MLOps, AWS/hybrid)

Responsibilities

• Drive adoption of enterprise AI-assisted engineering practices to improve code quality, delivery speed, and operational outcomes
• Apply SDLC toolchain knowledge to improve automation value
• Architect and implement scalable, fault-tolerant services with target-state architecture
• Design and deploy services with integration to enterprise systems while meeting security and governance requirements
• Lead and mentor the development team in a high-pressure delivery environment
• Collaborate with users, SMEs, architects, DBAs, and admins to design solutions and resolve issues
• Build and mature ML pipelines for fraud detection and risk assessment
• Deploy and validate ML models and ensure production readiness
• Design reusable ML platform components with monitoring and alerting for performance and reliability
• Develop agentic AI services to automate engineering and model-ops workflows
• Define guardrails and evaluation approaches for agentic AI in production

Skills

Java/J2EE

Spring Boot

AI-assisted development

Kubernetes

REST APIs

Microservices

Linux

Cloud (AWS)

MLOps

Observability

Security

Education

Bachelor's degree in Computer Science or related

Tools

Kafka

ELK/Splunk

Docker

CI/CD tools

Job description

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:

• Strong communication skills and proven ability to influence across senior technology and business stakeholders.
• Strong SDLC knowledge and agile ways of working, including CI/CD, application resiliency, security, testing, and operational stability.
• Agentic AI experience preferred: building and operating LLM-driven agents with tool integration, monitoring/telemetry, and governance/audit considerations.
• Experience with NoSQL databases such as Cassandra (preferred).