Software Engineer III

We have an exciting and rewarding opportunity for you to take your software engineering career to the next level. 

As a Software Engineer III at JPMorganChase within Consumer and Community Banking, 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

• Provide technical guidance and direction to business stakeholders, engineering teams, contractors, and vendors; influence technical decisions and outcomes.
• Develop secure, high-quality production code; review, troubleshoot, and debug code written by others to raise engineering standards.
• Drive adoption and governance of approved AI-assisted engineering practices (e.g., code review/refactoring, test acceleration, release readiness, incident/RCA) across teams.
• Establish and enforce measurable validation standards across delivery (secure coding, peer review, automated testing) and promote reuse of proven patterns and automation in the SDLC/TLM toolchain.
• Apply deep SDLC toolchain knowledge—including approved AI-assisted development and automation capabilities—to improve automation value at scale.
• Design and develop large-scale AWS cloud solutions/platforms aligned with firm-wide strategies and security controls.
• Deploy and enable enterprise cloud-based solutions supporting complex analytics and day-to-day business operations; build tooling to monitor, provision, automate, and report on services.
• Lead migration of legacy and big data applications to cloud-native architectures with zero downtime, improving reliability and operational performance.
• 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.
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Required qualifications, capabilities, and skills

• 4+ years of software engineering experience and Hands-on delivery across system design, application development, testing, and operational stability.
• Strong understanding of the SDLC toolchain, including approved AI-assisted development and automation, to drive automation at scale.
• Solid machine learning modeling knowledge from an engineering perspective.
• Advanced proficiency in one or more languages/frameworks (e.g., Python, Java) and related areas (big data, data pipelines, ML).
• Advanced knowledge of software applications/technical processes, with depth in at least one discipline (e.g., cloud, AI/ML, mobile).
• Strong grounding in application, data, and infrastructure architecture, including OOP/OOPS and SDLC best practices.
• Ability to solve design and functionality problems independently with minimal oversight, including practical cloud-native experience.
•  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

•  Cloud & Big Data Platforms: AWS certifications (e.g., Solutions Architect Associate), strong cloud technologies experience, and working knowledge of big data platforms.
• Data Engineering (Spark/Pipelines): Hands-on experience building data pipelines in Spark, including Spark query tuning/performance optimization.
• AI/ML & Automation Enablement: Knowledge of RAG architectures and exposure to AI/automation technologies that improve operations; proficient with Python ML/data ecosystem (Pandas, NumPy, etc.).