About the role
Own and execute: Drive the roadmap, execution, and delivery of multiple agentic systems across support automation, QA automation, documentation generation, and cross-org workflow automation. You are accountable for shipping production-quality agents on schedule. Architect agentic systems: Design multi-agent architectures involving LLM orchestration, tool-calling, retrieval-augmented generation (RAG), structured output parsing, and human-in-the-loop approval workflows. Stay hands-on: Write and review code daily. Debug agent failures, optimize prompts and pipelines, and troubleshoot integration issues alongside your engineers. Drive reliability and quality: Move beyond "it works in a demo." Own the iterative refinement of agent accuracy, hallucination detection, output validation, and safety guardrails. Define quality measurement frameworks, build observability and monitoring into every agent, and establish feedback loops that drive continuous improvement. Build for scale and security: Design agents that operate reliably at enterprise scale. Think critically about rate limiting, cost controls, data privacy, prompt injection prevention, and secure handling of sensitive information across integrations. Lead and grow
the team: Manage a team of engineers in a fast-paced, agile environment. Set technical direction, remove blockers, and mentor engineers in agentic system design and production best practices. Measure what matters: Define and track KPIs for each agent — resolution accuracy, coverage, time saved, adoption — and use data to prioritize what to build next. Experience with workflow orchestration tools (n8n, LangChain, LangGraph, CrewAI, or similar). Familiarity with RAG architectures, vector databases, and semantic search. Background in support engineering or QA — understanding the pain points these agents solve. Experience integrating with Salesforce, Jira/Confluence, or Slack APIs.
