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 environments
Takes initiative, translating strategy into action and driving delivery with a “roll up the sleeves” approach
Remains calm and structured under pressure, bringing clarity to high-stakes situations such as major incidents and critical decisions
Builds strong relationships and negotiates effectively with internal and external stakeholders, grounding decisions in data
Combines intellectual curiosity and a drive for excellence with a track record of success and a reputation as someone others enjoy working with
Brings strong attention to detail, a commitment to adopting and implementing best practices, and a bias toward continuous improvement
Embodies and delivers the firm’s values and culture towards colleagues, clients, and communities:
Win as one team
Lead with integrity
Be the change
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EEOC Statement
FIS is an equal opportunity employer. We evaluate qualified applicants without regard to race, color, religion, sex, sexual orientation, gender identity, marital status, genetic information, national origin, disability, veteran status, and other protected characteristics. The EEO is the Law poster is available here supplement document available here
For positions located in the US, the following conditions apply. If you are made a conditional offer of employment, you will be required to undergo a drug test. ADA Disclaimer: In developing this job description care was taken to include all competencies needed to successfully perform in this position. However, for Americans with Disabilities Act (ADA) purposes, the essential functions of the job may or may not have been described for purposes of ADA reasonable accommodation. All reasonable accommodation requests will be reviewed and evaluated on a case-by-case basis.
Sourcing Model
Recruitment at FIS works primarily on a direct sourcing model; a relatively small portion of our hiring is through recruitment agencies. FIS does not accept resumes from recruitment agencies which are not on the preferred supplier list and is not responsible for any related fees for resumes submitted to job postings, our employees, or any other part of our company.
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