Lead Architect - Cloud Financial Management, Reporting and AI

If you are excited about shaping how a leading financial institution understands and acts on its cloud spend, you can make a direct impact by building modern reporting and generative AI capabilities that drive measurable outcomes. You will join a Platform Operations team focused on delivering trusted cost, usage, and optimization insights across major cloud providers and private cloud environments. This role combines hands-on architecture with end-to-end product ownership, sitting at the intersection of cloud financial data, large language models, and enterprise reporting.

As a Lead Architect at JPMorganChase within Platform Operations and Infrastructure Platforms, you will own the product and architecture strategy for scheduled reporting and an agent-based assistant that help teams understand cost, usage, and optimization opportunities. You will translate complex cloud financial management needs into secure, scalable solutions that deliver proactive insights and conversational experiences. You will partner closely with engineering, finance, and cloud stakeholders to prioritize roadmaps, guide delivery, and ensure outcomes that matter across the firm.

This is a hands-on role that combines technical architecture with full product ownership. It sits at the intersection of cloud financial data, large language models, and enterprise reporting. You'll guide engineers across multiple squads, work closely with finance and FinOps leaders across lines of business, and set the technical direction for how CloudVision delivers insight both proactively and conversationally.

Job Responsibilities

- Own the product roadmap for scheduled reporting capabilities, including requirements, prioritization, and measurable success outcomes
- Lead the product direction for an agent-based assistant, shaping conversational experiences that help teams interpret cloud spend drivers and actions
- Design end-to-end architecture for scheduling, templating, subscriptions, audience management, delivery channels, and auditability
- Architect secure generative AI narratives and workflows that explain changes, highlight impact, and guide next-best actions
- Define evaluation and governance approaches for AI features, including accuracy testing, controls, and cost awareness for model usage
- Provide hands-on technical leadership across multiple squads, guiding design decisions, reviewing implementations, and improving reliability
- Partner with finance and cloud financial management leaders to understand needs, communicate progress, and drive adoption through clear value delivery
- Represent the product family in architecture and responsible AI forums, ensuring solutions align to security, data, and risk expectations
- Evaluate emerging agent tooling and workflow orchestration approaches to improve scalability, maintainability, and time-to-value

Required Qualifications, Capabilities, and Skills

- Formal training or certification on architecture concepts and 5+ years applied experience
- Demonstrated experience delivering cloud-native architecture and engineering outcomes, including design, development, testing, and operational stability
- Advanced proficiency in Python and at least one additional programming language used for production services
- Hands-on experience building large language model-powered applications, such as retrieval-augmented generation or text-to-SQL solutions, with systematic quality evaluation
- Experience designing scheduling, workflow, or notification systems with reliable delivery, retries, and end-to-end traceability
- Strong data modeling skills and experience working with large-scale financial or operational datasets
- Practical knowledge of public cloud platforms (such as Amazon Web Services, Microsoft Azure, or Google Cloud Platform) across compute, storage, database, and analytics services
- Proven ability to lead through influence, align cross-functional stakeholders, and solve complex problems with clear ownership and judgment
- Working knowledge of responsible AI practices, including access controls, data entitlements, and operating safely in regulated environments

Preferred Qualifications, Capabilities, and Skills

- Experience building or integrating Model Context Protocol tooling into agent workflows
- Experience with AI evaluation methods such as golden datasets, regression testing of prompts, and judge-based review approaches
- Experience measuring and managing model usage and cost in production environments
- Experience with Infrastructure as Code tools, such as Terraform
- Experience with large-scale data processing, data pipelines, and cloud data warehouse patterns
- Proficiency in Java and familiarity with Spring Boot and/or React for modern service and user interface development