Principal Solution Architect, Platform Engineering

FIS • Jacksonville, FL

1d

About The Position

The Principal Solution Architect, reporting to the Head of Architecture for Platform Engineering, designs and delivers enterprise platform capabilities that help product engineering teams build secure, reliable, performant, high-quality products for clients. Platform Engineering spans AI Engineering, API & Event Management, Front End, Site Reliability Engineering, Developer Experience & Quality, and Customer Identity & Access Management. The role focuses primarily on AI Engineering while also contributing across platform domains and potentially leading initiatives in any of them.

The role shapes patterns, platforms, and guardrails for embedding AI and agentic capabilities throughout the software development lifecycle (SDLC), moving teams from individual AI assistance toward governed, agent-driven workflows at enterprise scale. This includes AI-supported planning, design, coding, code review, testing, security remediation, release, and operations, alongside governance, security, measurement, and human oversight. The architect draws on hands-on software engineering and product delivery experience to translate complex business needs into practical solutions.

The position advances modernization and transformation by designing, documenting, and driving adoption of modern engineering patterns and platform capabilities. It brings cloud-native and AI-native approaches to platforms, develops roadmaps for modernization, and applies enterprise architecture practices including governance, ecosystem analysis, and vendor evaluation. The architect communicates architectural business value and provides thought leadership as the company transforms its engineering culture.

Requirements

• 10+ years of solution architecture and senior software engineering experience, including significant hands-on product delivery in a fast-paced, global software delivery organization operating with ambiguity.
• Expert understanding of software and internet architecture, including modern front-end and back-end stacks; API management; identity and access management; cloud-native and on-premises infrastructure, including containers, Kubernetes, and 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 such as retrieval-augmented generation, tool use, and multi-agent orchestration, context engineering, and model and agent quality evaluation, with hands-on use in real software delivery workflows.
• Strong understanding of SDLC risks introduced by AI, including 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 delivering platform capabilities that power consumer-facing software products, especially API- and event-driven systems.
• Deep experience adopting and operating within modern SDLC and engineering practices, including AI-enabled development, DevSecOps, CI/CD, GitOps, and platform-as-a-product.
• Thorough understanding of modern information security and data protection practices and their application 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.
• Exceptional communication skills and ability to navigate and influence at every organizational level, tailoring messages to different audiences.
• Sound judgment and flexibility, with the ability to consider broader context and strategy when developing solutions.
• Positive, adaptable approach to rapidly shifting priorities, topics, and environments.
• Initiative and ability to translate strategy into action and drive delivery with a hands-on approach.
• Ability to remain calm and structured under pressure, bringing clarity to high-stakes situations such as major incidents and critical decisions.
• Ability to build strong relationships and negotiate effectively with internal and external stakeholders, grounding decisions in data.
• Intellectual curiosity, a drive for excellence, a record of success, and a reputation as someone others enjoy working with.
• Strong attention to detail, commitment to adopting and implementing best practices, and a bias toward continuous improvement.

Nice To Haves

• Experience in financial services or other regulated industries, including model risk management, responsible AI practices, and emerging AI regulation and their influence on architecture decisions.
• Experience measuring engineering productivity and AI adoption impact using frameworks such as DORA metrics, and leading change management 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 major cloud providers' AI services, including trade-offs in model selection, hosting, and cost.
• Undergraduate degree in Engineering, Computer Science, or another technical field.

Responsibilities

• Contribute platform thinking and subject matter expertise across platform domains, applying modern engineering and architecture patterns to APIs, services, events, data, UI, and supporting capabilities.
• Create and communicate best-practice target-state designs using cloud-native patterns, supported by artifacts such as business case and process context, context and sequence diagrams, and architecture decision records.
• Lead 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.
• Define human-in-the-loop checkpoints and accountability for AI-generated changes.
• Define shared foundations for safe, scalable AI adoption, including governed model access and routing; agent orchestration and tool integration through standards such as MCP; retrieval over internal code and engineering knowledge; agent identity and least-privilege permissions; guardrails and audit trails; and evaluation, observability, and cost management.
• Partner with Platform Engineering leaders to introduce, socialize, and champion modernization of front-end, middle-tier, and back-end technology stacks within an AI-first delivery model.
• Lead evaluations of new and existing vendors for alignment with Platform Engineering strategy, technology approach, and integration patterns.
• Define and drive adoption of integration patterns across internal systems, services, and capabilities so quality data, events, and services are available across channels.
• Partner with platform owners, product partners, and stakeholders to intake and evaluate platform capabilities and features, influencing design decisions before execution and delivery.
• Validate designs through hands-on proofs of concept and reference implementations, and contribute to architecture reviews and governance.
• Articulate the business value of the architectural roadmap, including fact-based future perspectives, options, and trade-offs aligned with business and engineering strategy.
• Build modernization roadmaps and apply enterprise architecture practices such as governance, ecosystem analysis, and vendor evaluation.
• Demonstrate the firm's values and culture toward colleagues, clients, and communities: Win as one team, Lead with integrity, and Be the change.

What This Job Offers

• Job Type: Full-time
• Career Level: Principal
• Education Level: No Education Listed