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Lead Software Engineer - Cloud / GCP Engineer

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Onsite

Lead Software Engineer - Cloud / GCP Engineer

JPMC

Lead Software Engineer - Cloud / GCP Engineer

Skills

Google Cloud PlatformDockerPythonGolangMachine LearningAIAWSKubernetesCloud ArchitectTerraformAzure

1 month ago

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Important information


Contract type:

Permanent

Location:

Plano, TX, USA

Work mode:

Onsite

Published on:

24 September 2026

What they need


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 firm’s 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).

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