AI Engineer

Role Summary

We are seeking an AI Engineer II to design and deliver responsible secure AI-powered tools that improve onboarding (boarding) and operational execution. This role focuses on implementing agentic AI patterns (multi-step workflows tool-use and human-in-the-loop approvals) and integrating them into production systems to reduce manual effort increase consistency and improve service outcomes.

Key Responsibilities

• Build and enhance AI-assisted operational tools such as guided intake knowledge-grounded Q&A case/ticket summarization and runbook execution support.
• Implement agentic workflows with clear guardrails: input validation policy-aware prompting approval steps fallbacks and safe failure modes.
• Integrate AI capabilities with internal services and data sources through approved APIs; collaborate with platform teams to onboard integrations.
• Develop retrieval and grounding approaches (e.g. RAG) to ensure responses are based on approved knowledge sources with proper access controls.
• Create and maintain automated evaluations (quality groundedness safety) plus telemetry for monitoring performance cost and reliability.
• Contribute to code reviews unit/integration tests CI/CD and operational readiness (documentation alerts runbooks).
• Partner with operations SMEs product owners risk and security to refine requirements and ensure compliant delivery.

Required Qualifications

• Bachelors degree in Computer Science Engineering or equivalent practical experience.
• 2 years of software engineering experience building production services or applications.
• Demonstrated experience implementing LLM-powered features (prompting tool/function calling RAG agent/workflow orchestration).
• Proficiency in at least one of: Python Java or TypeScript; ability to work with REST APIs and event-driven patterns.
• Strong engineering fundamentals: data structures debugging testing and secure coding practices.
• Experience handling sensitive data responsibly and applying least-privilege access and secure SDLC practices.

Preferred Qualifications

• Experience supporting operational workflows (service management onboarding operations incident/change processes).
• Familiarity with search/retrieval systems ranking and content chunking/embedding strategies.
• Experience with observability (logs/metrics/traces) and operational support for production services.
• Knowledge of Responsible AI practices (privacy bias safety transparency) and implementing human-in-the-loop controls.
• Experience working in regulated environments (financial services a plus).

What Success Looks Like

• Delivers 1 2 production-ready AI capabilities that measurably improve cycle time quality or operational throughput.
• Establishes baseline evaluations and monitoring to prevent regressions and ensure safe operation.
• Produces clear documentation and supports smooth handoff/operations readiness.

Compliance & Responsible AI Expectations

• Adheres to Fiserv Responsible AI Guidelines and AI usage compliance requirements.
• Ensures appropriate handling of sensitive data (PII client data credentials) with auditability and access controls.
• Uses only approved AI platforms and integration patterns; evaluates emerging interoperability standards only when permitted by policy.

Internal policy reminders: MCP implementations are currently prohibited.