AgenticOps

TechDigital Group; Santa Clara (CA); Presencial; USD 180.000 - 240.000; Jornada completa

Descripción de la vacante

TechDigital Group is seeking a Senior Subject-Matter Expert for AgentOps/LLMOps to own the operations, observability, and lifecycle management of AI agents in production. You will ensure reliability, safety, and cost-efficiency while driving KPI performance across the Agent Factory.

You will define the AgenticOps framework, establish continuous evaluation, and implement strong observability. Collaboration with DevOps and AI/Data SMEs is essential to close the build-deploy-operate-improve loop.

Formación

• Extensive LLMOps/AgentOps experience in production environments.
• Hands-on with Google Cloud runtimes and observability tooling.
• Strong knowledge of model governance, safety, and HITL testing.
• Proficient Python with deep understanding of agent lifecycle and governance.

Responsabilidades

• Define and operate AgenticOps framework including registry and rollout controls.
• Establish continuous evaluation and monitoring of agent safety and cost.
• Implement observability/tracing for multi-agent systems and post-release processes.
• Design HITL supervision and automated pre-production simulations.
• Track KPIs via dashboards and governance mechanisms; optimize runtimes and budgets.
• Collaborate with DevOps and AI/Data SMEs to improve end-to-end lifecycle.

Conocimientos

LLMOps

AgentOps

MLOps

Python

GCP

Observability

SRE

Cost governance

Educación

Google Cloud Professional (ML/DevOps) certification

Herramientas

Vertex AI

Agent Engine

BigQuery

Looker

Descripción del empleo

Mandatory Skills: AgenticOps SME, LLMOps, MLOps, GCP

Role Summary

Senior subject‑matter expert for the operations, observability, and lifecycle management of AI agents in production ("AgentOps" / 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 AgenticOps 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 AgentScore / Agent 360 dashboards.
• Drive cost governance for agent runtimes: model tiering, context caching, batch/flex inference, budget caps and alerts.
• Collaborate with DevOps SME (deploy) and AI & Data SME (grounding) to close the build‑deploy‑operate‑improve loop; advise Client on AgenticOps ownership transfer.

Mandatory (Must-Have) Skills

• Strong LLMOps / MLOps / AgentOps 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.

Preferred (Good-to-Have) Skills

• Experience with ADK, A2A, MCP, and multi‑agent orchestration in production.
• BigQuery/Looker for agent analytics and KPI dashboards.
• Responsible‑AI, model governance, and audit/compliance frameworks.
• Prior enterprise‑scale AI platform operations experience.

Experience & Certifications

• 9–12+ years in ML/AI platform operations, SRE, or LLMOps with production agentic/GenAI exposure (Tier 5–6).
• Google Cloud Professional (ML/DevOps) certification preferred.