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. Required qualifications, capabilities, and skills 4+ yearsof software engineering experience and Hands-on delivery acrosssystem design, application development, testing, and operational stability. Strong understanding of theSDLC toolchain, includingapproved AI-assisted development and automation, to drive automation at scale. Solidmachine learning modelingknowledge from an engineering perspective. Advanced proficiency inone or more languages/frameworks(e.g.,Python, Java) and related areas (big data, data pipelines, ML). Advanced knowledge ofsoftware applications/technical processes, with depth in at least one discipline (e.g.,cloud, AI/ML, mobile). Strong grounding inapplication, data, and infrastructure architecture, includingOOP/OOPSand SDLC best practices. Ability to solvedesign and functionality problems independentlywith minimal oversight, includingpractical cloud-nativeexperience . 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), strongcloud technologiesexperience, and working knowledge ofbig data platforms. Data Engineering (Spark/Pipelines):Hands-on experiencebuilding data pipelines in Spark, includingSpark query tuning/performance optimization. AI/ML & Automation Enablement:Knowledge ofRAG architecturesand exposure toAI/automationtechnologies that improve operations proficient with Python ML/data ecosystem (Pandas, NumPy, etc.).