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Lead Software Engineer - Java, AWS, AI/ML
lead · Technology / Software Development
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
Preferred with event-driven architecture and streaming (e.g., Kafka) and data processing patterns for high-volume systems