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
- Robust 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.

📌 Agentic AI Engineering Manager (Noida)
🏢 DishTV
📍 Noida