Agentic AI Engineering Manager

Role & responsibilities

Build the agentic AI tower from zero: agents in the execution path of customer care, retention and field operations orchestration, tool integration, eval harnesses, guardrails, cost control and hire the developer team.

Preferred candidate profile

Strong backend/ML engineer LLM application builder led a small AI team or platform (8–12 years overall). The spine is software engineering; the last 2–3 years are production LLM systems

Mandatory do not submit without these

• Shipped LLM-powered systems to PRODUCTION with real users demos and POCs do not qualify
• Agent patterns hands-on: tool/function calling, MCP-style integration, orchestration, structured outputs
• Built evaluation harnesses and guardrails can explain how they KNEW the system worked and what it cost
• Strong engineering spine: Python and/or TypeScript/Java, APIs, queues, databases — an engineer first
• Has hired and led engineers; built a team or practice from zero

Nice to have — tie-breakers, not filters

• Claude/Anthropic API depth (or equivalent frontier-model platform mastery)
• Contact-center / voice-bot / WhatsApp-channel AI; RAG over messy enterprise data
• LLM cost-and-latency engineering at scale; MLOps exposure

Stack we look for on the CV: Frontier LLM APIs (Claude-class) • MCP / tool calling • RAG + vector stores • eval frameworks • Python + TypeScript • Kafka/REST plumbing • Kubernetes.