at J.P. Morgan
Bulge Bracket Investment BanksPosted 7 days ago
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**Lead Software Engineer - Cloud/GCP Engineer** drives agile teams to develop secure, scalable solutions. Leads GCP architecture, automates onboarding with Terraform, manages IAM security, integrates identity federation, and advocates AI-assisted engineering. Must have 5+ years of software engineering experience, GCP IAM expertise, and hands-on proficiency in GCP services, Terraform, and Python/Go.
- Compensation
- Not specified
- City
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- Country
- United States
Currency: Not specified
Full Job Description
Location: Plano, TX, United States
We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible.
As a Lead Software Engineer at JPMorganChase within the Corporate Sector - Infrastructure platform, you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. As a core technical contributor, you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firms business objectives.
Job responsibilities
As a Lead Software Engineer at JPMorganChase within the Corporate Sector - Infrastructure platform, you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. As a core technical contributor, you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firms business objectives.
Job responsibilities
- Executes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or breakdown technical problems
- Leads the architecture and hands-on delivery of GCP foundational platform capabilities (landing zones, org/folder/project hierarchy, networking, shared services).
- Builds and maintain reusable automation and paved roads (Terraform modules, reference architectures, CI/CD enablement) that accelerate secure onboarding.
- Owns GCP security guardrails: IAM/role engineering, service account patterns, IAM Conditions, org policies, and deny policies.
- Designs and implements identity federation (OIDC/SAML, Workforce Identity Federation, Workload Identity Federation) and enable IAM governance/audit readiness through automation and evidence patterns.
- 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.
- Leads evaluation sessions with external vendors, startups, and internal teams to drive outcomes-oriented probing of architectural designs, technical credentials, and applicability for use within existing systems and information architecture
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and 5+ years applied experience
- Strong experience with GCP IAM and identity (role/permission modeling, service accounts, federation patterns, access governance).
- Hands-on expertise with key GCP services such as Cloud Run/Cloud Functions, Pub/Sub, and GKE.
- Proficiency in Terraform and programming/scripting in Python or Go (automation, tooling, CI/CD integration).
- Experience implementing guardrails at scale (IAM Conditions/deny policies, policy-as-code, centralized logging/audit, key management patterns).
- Demonstrated ability to deliver secure, compliant solutions and influence technical decisions across cross-functional teams.
- 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
- Experience with AWS and/or Azure (multi-cloud operating models and patterns).
- Strong containerization/orchestration experience (Docker, Kubernetes), including production-grade GKE operations.
- Solid understanding of OAuth2, OIDC, and SAML; secure service-to-service identity patterns.
- Good understanding of LLM and other AI/ML frameworks which can be used in AIOPS
- Relevant certifications (e.g., Google Professional Cloud Architect and/or Professional Cloud Security Engineer).
