Lead Software Engineer - Java, AWS, AI/ML

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

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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

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

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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.

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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.

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

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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

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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

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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

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

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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)

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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

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

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

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

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

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

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

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

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

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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.

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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

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Preferred AWS Certification

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

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