Software Engineering III - AWS, GCP, Terraform

Jersey City, NJ, United States

• Job Identification
• Job Category Software Engineering
• Business Unit Corporate Sector
• Posting Date 10/16/2026, 06:24 PM
• Locations 575 Washington Blvd, Jersey City, NJ, 07310, US
• Job Schedule Full time
• Job Shift Day
• Base Pay/Salary Jersey City,NJ $-$

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 JPMorgan

Chase within the Corporate Technology team, 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

• Implementation of key components of a public cloud platform, primarily on AWS.
• Develop secure, high-quality production code and infrastructure automation (primarily in Python and Terraform), and review and debug code written by others to ensure correctness, performance, and maintainability.
• Influence product design, application functionality, and technical operations within the team and domain by proposing pragmatic architectures, tradeoffs, and standards aligned to firm SDLC, security, and controls expectations.
• Partner with operations, SRE, security, risk, and controls stakeholders to deliver compliant solutions, improve observability, reduce operational toil, and ensure audit-ready processes and artifacts.
• Drive automation and CI/CD improvements, including pipeline reliability, quality gates, testing strategy, and repeatable environment provisioning to support safe and fast delivery.
• 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.
• Use AI-assisted developer tools, including Git Hub Copilot and Microsoft Copilot, to accelerate routine engineering tasks (for example, scaffolding, small utilities, repetitive patterns, test generation, refactoring suggestions, and first-pass documentation), while ensuring all outputs are validated and refined to meet production and security standards.

Define and reinforce safe team usage patterns for AI-assisted development, including verification expectations (correctness, security implications, licensing/IP considerations, and compliance) and adherence to firm controls (for example, avoiding sensitive data in prompts and ensuring reviews and approvals occur before merge and release).

Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts and 4+ years applied experience
• Hands-on practical experience in developing and designing software on AWS and GCP, with meaningful hands-on experience delivering solutions involving AWS database services and the infrastructure that supports them.
• Strong, practical proficiency in Python and Terraform, including building and maintaining production-grade automation and infrastructure as code.
• Advanced working knowledge of AWS and GCP services across traditional compute, containerized workloads, and serverless architectures, and how these patterns integrate with database services and platform guardrails.
• 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
• Working experience with engineering tool chains including Jira, Bitbucket, and Confluence, and…