Technical Specialist
HCLTech · Bengaluru, Karnataka, India
Applications are made on HCLTech's own site. We never sit in the middle.
Job description
Technical Specialist India Technical Specialist Bengaluru, Karnataka Job 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 Intel 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 Intel on AgenticOps ownership transfer. Skill Requirements 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. Other Requirements 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. 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. Offshore (India) with US overlap, or Onsite (USA) as required.