JPMorgan Chase·BFSI·9 days ago
9 days ago
Lead Architect: AI enablement
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What you'll do
- 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
What they're looking for
- 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 have
- 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
Summarised by NextRaise from the employer’s description, which follows in full below.
Full description from employer
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.
Job responsibilities
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.
Job 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
Required qualifications, capabilities, and skills
- 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
Preferred qualifications, capabilities, and skills
- 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
BFSI
Company
JPMorgan ChaseBFSI
Plano, United States of America
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