AI Solutions and Platforms Operations Engineer

Responsibilities

1. AI Agent Operations Center (70%)
   • Build “operations center” capabilities for agent runtime management: agent registry, versioning, deployment tracking, and run histories
   • Enable operational workflows such as incident triage, replay/debug runs, trace correlation, and root-cause analysis across agent steps
   • Implement operational dashboards and views for agent health: success rate, latency, tool failure rate, cost per run, and loop detection
   • Instrument agent flows end-to-end using OpenTelemetry (or equivalent), enabling correlation across prompts, tool calls, retrieval, and responses
   • Implement semantic conventions and tagging standards (agent name/version, tool name, model provider, environment, tenant/app)
   • Partner with SRE/observability teams to ensure production-grade monitoring, alerting, and operational readiness
2. Collaboration with Teams (10%)
   • Collaborate with transformation teams and business stakeholders to understand requirements and tailor AI agents to specific domains.
   • Work closely with AI platform teams to build scalable and cross-domain AI agents while ensuring end-to-end observability.
3. Integration & Deployment (10%)
   • Build and maintain CI/CD pipelines for agent services and operations center components, including automated testing and deployment
   • Automate onboarding for new agent use cases (templates, scaffolding, configuration checks)
   • Drive best practices for secure, scalable, and cost-effective agent deployments
4. Continuous Learning (10%)
   • Stay updated with the latest advancements in AI and machine learning technologies and integrate these into existing or new AI agents.
     • Conduct thorough testing and validation to ensure the reliability and accuracy of AI agents and solutions.