Senior Lead Architect, Agentic AI Solutions Architect

Drive significant business impact and help shape the target state architecture in multiple architecture domains

If you are excited about shaping the future of technology and driving significant business impact in financial services, we are looking for people just like you. Join our team and help us develop game-changing, high-quality solutions. As a Senior Lead Architect at JPMorganChase within the Corporate Sector, Employee Platforms, you are an integral part of a team that works to develop high-quality architecture solutions for various software applications and platform products. You drive significant business impact and help shape the target state architecture through your capabilities in multiple architecture domains. You will help shape, prototype, and scale agentic AI solutions across a global enterprise serving more than 300,000 employees. This individual contributor role sits within the central Engineering and Architecture function of a 3000+ person technology organization, partnering horizontally across Employee Platforms to design intelligent, secure, and practical AI-enabled capabilities. You will work across domain architects, engineers, product teams, security, control functions, and platform owners to architect and build custom agentic systems, implement AI solutions in public cloud environments, and create reusable patterns that help teams provide employees innovative AI solutions that allow them to deliver at their highest potential. The role balances future-facing agentic architecture with hands-on delivery, helping Employee Platforms evaluate trade-offs, apply appropriate controls, and deliver measurable value through both custom and vendor AI capabilities. Job responsibilities Represents a product family in technical governance bodies and proposes improvements to architecture governance practices Regularly provides technical guidance and direction to support the business and its technical teams, contractors, and vendors Leverages enterprise-authorized AI capabilities within the work environment to accelerate architecture analysis and decisioning across the product family, with human-in-the-loop validation and appropriate handling of sensitive data Guides evaluation of current and new technologies using existing standards and frameworks, influencing peers and decision-makers to adopt leading-edge solutions where appropriate Drives decisions that influence product design, application functionality, and technical operations and processes Develops secure and high-quality production code, and reviews and debugs code written by others Serves as a function-wide subject matter expert and contributes to the engineering community as an advocate of firmwide SDLC frameworks and practices Establishes reuse-first AI-enabled engineering patterns and governance across SDLC/toolchain practices, ensuring traceability/auditability, resiliency, and security controls Architect and build custom agentic systems that support Employee Platforms use cases across employee experience, workflow automation, knowledge discovery, service operations Design prototypes, reference implementations, reusable patterns, and evaluation harnesses that help engineering teams move agentic AI capabilities safely from concept to production Implement AI and agentic solution patterns in public cloud environments, with practical attention to APIs, data access, deployment automation, observability, resilience, and operational support Required qualifications, capabilities, and skills Formal training or certification on architecture concepts and 5+ years applied experience Hands-on practical experience delivering system design, application development, testing, and operational stability Advanced in one or more programming language(s) and deep expertise in software architecture, applications, and technical processes within one or more technical disciplines (e.g., cloud, artificial intelligence, machine learning, mobile, etc.) Demonstrated experience applying enterprise-authorized AI capabilities within the work environment in architecture and engineering workflows with strong validation habits and awareness of data sensitivity Ability to evaluate and integrate AI-enabled capabilities into enterprise-grade architectures while meeting resiliency, security, and auditability requirements Ability to tackle design and functionality problems independently with little to no oversight Ability to evaluate current and emerging technologies to select or recommend the best solutions for the future state architecture Strong judgment and communication skills to influence technical direction across teams Agentic AI architecture: Hands-on experience architecting and building custom agentic systems, including tool use, orchestration, memory patterns, planning, autonomy boundaries, human-in-the-loop controls, and evaluation approaches Public cloud implementation: Practical experience implementing AI, data, integration, or agentic solutions in public cloud environments AI / ML foundations: Strong understanding of LLMs, RAG, embeddings, model selection, prompt design, multi-model orchestration, and responsible use of generative AI capabilities Preferred qualifications, capabilities, and skills Experience with vendor AI platforms and enterprise SaaS ecosystems, such as Microsoft Copilot, ServiceNow, Salesforce, Oracle HCM, OpenAI, Anthropic Claude, or similar platforms Experience operating in a regulated enterprise environment, including financial services, healthcare, government, or other control-oriented industries Familiarity with security, SaaS integration, AI governance, responsible AI practices, model risk concepts, and control frameworks for autonomous or semi-autonomous systems Experience contributing to reusable architecture patterns, engineering standards, architecture communities, or internal enablement materials Exposure to emerging agentic AI protocols, frameworks, and integration patterns, including MCP, A2A, LangGraph, CrewAI, or similar technologies Mentor engineers and architects, contribute to architecture communities, and communicate complex AI architecture concepts in a clear, practical, and inspiring way Communication and influence: Ability to explain complex technical tradeoffs clearly to engineering, product, architecture, security, and leadership stakeholders, with strong written architecture documentation skills