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.).
