Principal Solution Architect, Platform Engineering

Job description

Job Description About the role Reporting to the Head of Architecture for Platform Engineering, the Principal Solution Architect plays a crucial role in designing and delivering enterprise platform capabilities. Platform Engineering is chartered to deliver the shared capabilities that product engineering teams use to deliver secure, reliable, performant, high-quality products to clients faster. Platform Engineering’s portfolio spans AI Engineering, API & Event Management, Front End, Site Reliability Engineering (SRE), Developer Experience & Quality, and Customer Identity & Access Management. This role’s primary focus is AI Engineering: architecting how AI and agentic capabilities are built into the software development lifecycle (SDLC) so that engineering teams can deliver higher-quality software faster and more safely. You will shape the patterns, platforms, and guardrails that move teams from individual AI assistance to governed, agent-driven workflows at enterprise scale. Like every architect on the team, you will also contribute across all platform domains and may lead initiatives in any of them, so broad, enterprise-level platform thinking and engineering excellence are as essential as AI depth. What you will be doing You will make a significant impact on the company’s modernization and transformation journey by designing, documenting, and driving adoption of modern engineering patterns for next-generation platform capabilities and the ways product teams consume them. AI-first delivery is central to that journey. Your focus spans the full SDLC, from AI-assisted planning, design, and coding, through automated code review, testing, and security remediation, to agent-supported release and operations, along with the governance, security, and measurement that allow the enterprise to trust and scale these capabilities. You will draw on significant hands-on software engineering experience in product delivery to turn complex business needs into thoughtful, practical solutions. You will bring cloud-native and AI-native patterns to our platforms, build roadmaps that clearly sequence the steps of our modernization journey, and apply enterprise architecture practices such as governance, ecosystem analysis, and vendor evaluation, while providing thought leadership on what is possible as we transform engineering culture at FIS. Specifically, you will be responsible for: Contributing platform thinking and subject matter expertise across all platform domains, applying modern engineering and architectural patterns to design systems (APIs, services, events, data, UI, and supporting capabilities) that enable engineering teams to deliver product capabilities rapidly Creating and communicating best-practice target-state designs that use cloud-native patterns and are easy for engineering teams to consume, supported by clear artifacts such as business case and process context, context and sequence diagrams, and architecture decision records Leading the architecture and roadmap for AI and agentic capabilities across the SDLC, including requirements and design assistance, AI-assisted coding, automated code review and test generation, vulnerability and dependency remediation, documentation, and release and operational support, with clearly defined human-in-the-loop checkpoints and accountability for AI-generated changes Defining the shared foundations engineering teams use to adopt AI safely and at scale, including governed model access and routing, agent orchestration and tool integration through open standards such as the Model Context Protocol (MCP), retrieval over internal code and engineering knowledge, agent identity and least-privilege permissions, guardrails and audit trails, and evaluation, observability, and cost management of AI usage Partnering with leaders across the Platform Engineering organization to introduce, socialize, and champion modernization of front-end, middle-tier, and back-end technology stacks within an AI-first delivery model Leading evaluations of new and existing vendors for fit with Platform Engineering’s strategy, technology approach, and integration patterns Defining and driving adoption of integration patterns across internal systems, services, and capabilities so that quality data, events, and services are available for consumption regardless of channel Partnering with platform owners, product partners, and other stakeholders on the intake and evaluation of platform capabilities and features, influencing design decisions and approaches before execution and delivery begin Validating designs through hands-on proofs of concept and reference implementations, and contributing to architecture reviews and governance across the team Articulating the business value of the architectural roadmap, including fact-based “over the horizon” perspectives with options and trade-offs that align to the broader business and engineering strategy What you will need 10+ years of solution architecture and senior software engineering experience, including significant hands-on product delivery, in a fast-paced, global software delivery organization that operates with ambiguity Expert understanding of software and internet architecture, including modern front-end and back-end stacks, API management and identity and access management, cloud-native and on-premises infrastructure (containers, Kubernetes, infrastructure as code), networking and internet connectivity, and browser and mobile application delivery Deep expertise designing and delivering AI-enabled engineering capabilities, including AI coding assistants and agents, LLM application patterns (retrieval-augmented generation, tool use, and multi-agent orchestration), context engineering, and evaluation of model and agent quality, with hands-on experience applying them to real software delivery workflows Strong understanding of the risks AI introduces into the SDLC, such as sensitive data exposure, prompt injection, insecure or improperly licensed generated code, and over-privileged agents, and how to mitigate them through identity, policy, human oversight, and auditability Demonstrated track record of delivering platform capabilities that power consumer-facing software products, especially API- and event-driven systems Deep experience adopting and operating within a modern SDLC and engineering practice (AI-enabled development, DevSecOps, CI/CD, GitOps, and platform-as-a-product) Thorough understanding of modern information security and data protection practices and how to apply them across technology stacks in regulated environments Collaborative, low-ego approach focused on business outcomes, with the ability to motivate and energize others and influence without authority to achieve common goals Exceptional communication skills and can navigate and influence at every level of the organization, tailoring the message to any audience to achieve the desired outcome Preferred qualifications Experience in financial services or other regulated industries, including model risk management, responsible AI practices, and emerging AI regulation, and how they shape architecture decisions Experience measuring engineering productivity and the impact of AI adoption using frameworks such as DORA metrics, and leading the change management needed to drive adoption at scale Experience applying AI to modernize legacy platforms and codebases, such as mainframe or large monolithic applications Hands-on experience with multiple model providers, agent frameworks, and the AI services of major cloud providers, including trade-offs in model selection, hosting, and cost Undergraduate degree in Engineering, Computer Science, or another technical field Personal characteristics Demonstrates sound judgment and flexibility, thinking comprehensively about issues and developing solutions that account for the broader context and strategy Maintains a positive, adaptable approach to rapidly shifting priorities, topics, and