AgenticOps SME

AgenticOps SME – LLMOps / MLOps / Google Cloud Platform

Santa Clara, CA

Role Summary

• Senior AgenticOps / LLMOps / MLOps Subject Matter Expert responsible for operating, monitoring, and continuously improving production AI agents.
• Own the AgenticOps framework across the AI Agent Factory, ensuring reliability, safety, observability, cost efficiency, governance, and business KPI performance.
• Lead the complete agent lifecycle from validation and deployment through production monitoring, evaluation, optimization, and continuous improvement.

Key Responsibilities

• Define and operate AgenticOps frameworks covering agent registry, versioning, controlled rollouts, rollback, and lifecycle governance.
• Establish continuous evaluation and monitoring for quality, autonomy, safety, latency, cost, reuse, and reliability.
• Implement observability and distributed tracing for multi-agent systems using Google Cloud Agent Engine, Cloud Monitoring, Cloud Logging, and Cloud Trace.
• Own the validation-to-production gate process and manage post-production issues and escape remediation.
• Design Human-in-the-Loop (HITL) supervision, feedback mechanisms, and automated pre-production simulations.
• Track agent business KPIs such as CSAT, TAT, MTTR, cost savings, and operational performance through dashboards and analytics.
• Drive LLM/agent cost optimization through model tiering, context caching, batch/flex inference, budget controls, and cost alerts.
• Partner with DevOps, AI, and Data teams to establish an effective build → deploy → operate → improve lifecycle.
• Provide technical guidance on AgenticOps operating models, ownership transition, governance, and enterprise adoption.

Mandatory Skills

• Strong hands-on experience with LLMOps, MLOps, AgentOps, or AI platform operations in production.
• Extensive experience operating GenAI and agentic AI systems in enterprise environments.
• Hands-on expertise with Google Cloud Vertex AI, Agent Engine, and Google Cloud Platform observability tools.
• Strong knowledge of AI/agent evaluation frameworks, guardrails, Model Armor, HITL, prompt testing, and robustness testing.
• Experience with monitoring, distributed tracing, reliability engineering, and SRE practices for AI workloads.
• Strong understanding of LLM/agent performance and cost optimization.
• Proficiency in Python.
• Strong understanding of agent lifecycle management, governance, reliability, and production operations.

Preferred Skills

• Experience with ADK, A2A, MCP, and multi-agent orchestration in production.
• Experience using BigQuery and Looker for agent analytics and KPI reporting.
• Knowledge of Responsible AI, model governance, audit, compliance, and AI risk frameworks.
• Experience supporting enterprise-scale AI/ML platforms.

Experience & Certifications

• 9–12+ years of experience in ML/AI platform operations, SRE, MLOps, or LLMOps, with significant production GenAI/agentic AI experience.
• Google Cloud Professional certification in Machine Learning, DevOps, or a related discipline is preferred.