AgenticOps
Job in
Santa Clara, Santa Clara County, California, 95053, USA
Listed on 2026-10-08
Listing for:
TechDigital Group
Full Time
position Listed on 2026-10-08
Job specializations:
-
Software Development
AI Engineer (Applied/Software), AI Reliability/ Performance Engineer
Job Description & How to Apply Below
Skills:
Agentic Ops SME, LLMOps, MLOps, GCP Role Summary
Senior subject‑matter expert for the operations, observability, and lifecycle management of AI agents in production ("Agent Ops" / LLMOps). Owns the frameworks and practices to safely deploy, monitor, evaluate, and continuously improve live agents — ensuring reliability, safety, cost‑efficiency, and business-KPI performance across the Client Agent Factory.
Key Responsibilities- Define and operate the Agentic Ops framework: agent registry, versioning, guarded rollout, and rollback for production agents.
- Establish continuous evaluation and monitoring: quality, autonomy, safety (guardrails, Model Armor), latency, cost, and reuse metrics.
- Implement observability and tracing for multi-agent systems (Agent Engine Observability, Cloud Monitoring/Logging/Trace).
Own the 5-gate validation-to-production process and post-release escape management for delivered agents. - Design human‑in‑the‑loop (HITL) supervision, feedback loops, and automated pre‑production simulations for safe rollout.
- Track and report agent business KPIs (CSAT, TAT, MTTR, cost savings) via Agent Score / Agent 360 dashboards.
- Drive cost governance for agent runtimes: model tiering, context caching, batch/flex inference, budget caps and alerts.
- Collaborate with Dev Ops SME (deploy) and AI & Data SME (grounding) to close the build‑deploy‑operate‑improve loop; advise Client on Agentic Ops ownership transfer.
- Strong LLMOps / MLOps / Agent Ops experience operating GenAI or agentic systems in production.
- Hands‑on with Google Cloud agent runtimes:
Vertex AI, Agent Engine, and observability tooling. - Agent evaluation and safety: eval frameworks, guardrails, Model Armor, HITL, prompt/robustness testing.
- Monitoring, tracing, and reliability engineering (SRE) for AI workloads.
- Cost governance and performance tuning for LLM/agent workloads.
- Proficiency in Python; strong grasp of agent lifecycle and governance.
- Experience with ADK, A2A, MCP, and multi‑agent orchestration in production.
- Big Query/Looker for agent analytics and KPI dashboards.
- Responsible‑AI, model governance, and audit/compliance frameworks.
- Prior enterprise‑scale AI platform operations experience.
- 9–12+ years in ML/AI platform operations, SRE, or LLMOps with production agentic/GenAI exposure (Tier 5–6).
- Google Cloud Professional (ML/Dev Ops) certification preferred.
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