Solutions Architect - AI-Powered Developer Tooling

About the role

Fiserv, a global leader in fintech and payments, is looking for a Solutions Architect to own the end-to-end architecture of internal platforms and tools that use AI to reduce human effort across the software lifecycle (building, testing, managing and operating software). The role defines the technical vision and standards, guides engineering teams from concept through production, and bridges business stakeholders, security and compliance functions and engineering teams. It is a hands-on architecture role, with expectations to be a power user of AI tooling and to prototype where needed.

Key responsibilities

• Own the end-to-end architecture of AI-powered developer tooling and agentic platforms, from data and model layers through APIs, orchestration, integration and delivery.
• Define technical vision, reference architectures and standards, and make and document key design decisions (ADRs).
• Design agentic and LLM-based systems, covering model selection, retrieval (RAG), tool/function calling, orchestration, evaluation and guardrails.
• Architect for non-functional requirements (scalability, latency, cost, resilience, observability) and for security, privacy and auditability in a regulated financial environment.
• Lead platform-integration decisions and evaluate AI models, frameworks and vendors.
• Mentor engineers, review designs and code, and partner with stakeholders across business, security, compliance and infrastructure.
• Use AI tooling hands-on to prototype, validate patterns and set the standard for daily use.

Required profile

• 12+ years of software engineering experience, including at least 3 years in a Solution Architect, Technical Architect or Technical Lead capacity.
• Proven ownership and delivery of end-to-end system architecture in production, across data, application, integration and infrastructure layers.
• Strong experience designing and implementing AI/LLM-powered solutions, including RAG, agentic workflows, orchestration, tool calling, model selection, evaluation frameworks and AI guardrails.
• Hands-on practice with AI development tools, coding assistants and agent frameworks, with expertise in prompt engineering and context management.
• Demonstrated track record of applying AI and LLMs in real-world systems, supported by production deployments, projects or portfolio work.

Required skills

• Cloud-native and distributed systems architecture on AWS, Azure and/or GCP.
• Microservices-based applications with containerization (Docker and Kubernetes), API-first and event-driven patterns.

To apply, use your legal name, complete the online profile and attach your resume (one or both is acceptable).