AI Engineer

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

Bachelor’s 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.