Lead Architect: AI enablement

JPMorgan Chase & Co.•Plano, TX

About The Position

A career with us is a journey, not a destination. This could be the next best step in your technical career. Join us. As a Lead Architect at JPMorgan Chase within the Chief Technology Office (CTO) - AI4Tech Scaling team, you are an integral part of a team that works to develop high-quality architecture solutions for various software applications on modern cloud-based technologies. As a core technical contributor, you are responsible for conducting critical architecture solutions across multiple technical areas within various business functions in support of project goals.

Requirements

Formal training or certification on software engineering concepts and 5+ years applied experience
Hands-on experience delivering system design, application development, testing, and operational stability
Working knowledge and experience in backend development either Python and/or Java
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
Understands LLM-based developer tools in context management, RAG patterns, and responsible AI guardrails
Experience with CI/CD pipelines (Jenkins, Spinnaker) and automation frameworks
Familiar with containerization (Docker, Kubernetes/EKS) and serverless architectures
Active knowledge of agile methodologies, application resiliency, and security
Practical cloud native experience and understanding of the software development lifecycle

Nice To Haves

Enterprise AI-assisted development platform architecture — designs and scales GitHub Copilot, Copilot Enterprise, and Copilot Extensions rollouts across large developer populations, including SSO, license governance, and usage attribution
LLM integration and agentic workflow design — reference architectures for Model Context Protocol (MCP) servers, Copilot Extensions/Skills, RAG pipelines, and multi-agent orchestration for internal developer tooling
AI governance, safety, and compliance — prompt injection defense, data-leak prevention, content filtering, model routing, and audit/telemetry patterns aligned with regulated-industry (finance) responsible-AI standards
Architecture leadership — authors ADRs, C4 diagrams, and reference architectures; chairs architecture review boards; mentors senior engineers and influences technical strategy across the organization

Responsibilities

Administer and scale GitHub Copilot and Claude Code platforms across the enterprise with user provisioning, policy enforcement, and usage analytics
Develop and maintain custom Copilot Skills (i.e., .instructions.md, SKILL.md, etc.) and Agent configurations to encode domain-specific knowledge and team workflows
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
Design and provision cloud infrastructure using Terraform and AWS services, ensuring reliability and compliance
Develop internal tooling, automation pipelines, and self-service portals to support AI developer tool adoption
Develop secure, high-quality production code, and review and debug code written by others
Build reporting dashboards and metrics pipelines to track AI tool usage, productivity impact, and cost optimization
Identify opportunities to eliminate or automate remediation of recurring issues to improve operational stability