Lead Software Engineer - Java, AWS, AI/ML

JPMorgan Chase & Co.; Jersey City (NJ); On-site; USD 150,000 - 210,000; Full time

Job summary

JPMorgan Chase & Co. in Jersey City seeks a Lead Software Engineer to design and deliver trusted, scalable technology products within Corporate technology - Instrument Reference Data.

You will work on secure, high-quality production code and guide agile teams across multiple technical areas to achieve firm objectives. You will drive AI-assisted engineering practices, establish validation standards, and promote pattern reuse while focusing on reliability, security, and performance across software

Qualifications

• Formal training or certification on software engineering concepts.
• Experience delivering system design, application development, testing, and operational stability.
• Experience with micro-services architecture, design patterns and technologies Java, Spring boot, Kafka, Hibernate.
• Experience building cloud-native solutions on AWS (compute, networking, storage, security) and deploying containerized services (e.g., ECS).
• Experience with processing of large data volumes and data analysis using SQL/NoSQL
• Experience with CI/CD, infrastructure-as-code, and automation to enable reliable releases and environment consistency
• Experience in authentication/authorization, secrets management, encryption, and secure coding practices
• Experience in Agile methodologies and collaboration across product, data, platform/SRE, and governance stakeholders
• 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

Responsibilities

• Executes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or breakdown technical problems
• Develops secure and high-quality production code, and reviews and debugs code written by others
• 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
• Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems
• Leads evaluation sessions with external vendors, startups, and internal teams to drive outcomes-oriented probing of architectural designs, technical credentials, and applicability for use within existing systems and information architecture
• 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
• Designs and delivers scalable ML systems (batch and real-time inference), including data/feature pipelines, model training, evaluation, deployment, monitoring, and drift/performance management
• Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and ML systems (alerts, SLOs, auto-rollbacks, guardrails)
• Leads communities of practice across Software Engineering and AI/ML to drive awareness and use of new and leading-edge technologies (MLOps, LLM patterns, feature stores, observability, model monitoring)

Skills

Java

Spring Boot

Kafka

Hibernate

AWS

CI/CD

AI-assisted Dev

Microservices

Security

Agile

Tools

ECS

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 JPMorgan Chase within the Corporate technology - Instrument Reference Data, you serve as a seasoned member of an agile team to design and deliver trusted market-leading technology products in a secure, stable, and scalable way. You are responsible for carrying out critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives.

Job responsibilities

• Executes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or breakdown technical problems




• Develops secure and high-quality production code, and reviews and debugs code written by others




• 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.




• Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems




• Leads evaluation sessions with external vendors, startups, and internal teams to drive outcomes-oriented probing of architectural designs, technical credentials, and applicability for use within existing systems and information architecture




• 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




• Designs and delivers scalable ML systems (batch and real-time inference), including data/feature pipelines, model training, evaluation, deployment, monitoring, and drift/performance management




• Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and ML systems (alerts, SLOs, auto-rollbacks, guardrails)




• Leads communities of practice across Software Engineering and AI/ML to drive awareness and use of new and leading-edge technologies (MLOps, LLM patterns, feature stores, observability, model monitoring)

Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts and 5+ years applied experience




• Hands-on practical experience delivering system design, application development, testing, and operational stability




• Experience with micro-services architecture, design patterns and technologies Java, Spring boot, Kafka, Hibernate




• Experience building cloud-native solutions on AWS (compute, networking, storage, security) and deploying containerized services (e.g., ECS).




• Experience with processing of large data volumes and data analysis using SQL/NoSQL




• Experience with CI/CD, infrastructure-as-code, and automation to enable reliable releases and environment consistency




• Experience in authentication/authorization, secrets management, encryption, and secure coding practices




• Experience in Agile methodologies and collaboration across product, data, platform/SRE, and governance stakeholders




• 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

Preferred qualifications, capabilities, and skills

• Preferred AWS Certification




• Preferred building AI/GenAI services (RAG, agent/tool orchestration, evaluation frameworks, guardrails) in production




• Preferredwith event-driven architecture and streaming (e.g., Kafka) and data processing patterns for high-volume systems