Software Engineer III

Description 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. • 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.).