Description
Our client is looking for Agentic AI Engineer to build and deploy AI agents that automate real business workflows — reducing operational cost, headcount dependency, and turnaround time. This is a hands-on implementation role, not R&D.; If your experience lives in notebooks, courses, and demos, this role is not for you.

What You'll Own
Design and build multi-agent systems that replace or augment manual business processes
Integrate agents with internal tools (CRM, ERP, databases, APIs, communication platforms)
Optimize agent performance for cost per task — token efficiency, latency, and accuracy
Set up observability and monitoring so agents don't fail silently in production
Work directly with business stakeholders to translate process pain points into agent workflows
Maintain and iterate on deployed agents — not a build-and-forget role

Requirements
Minimum 2 production-deployed agentic systems — not POCs, not demos, not coursework. Real systems, real users, real business impact. You must be able to speak to what broke, how you fixed it, and what it saved




Proficiency in Python, LangChain/LangGraph or equivalent orchestration frameworks
Experience with tool-calling, RAG pipelines, memory management, and multi-agent coordination
Familiarity with MCP servers and API integrations
Cost-aware engineering mindset — can justify model choice (when to use GPT-4o vs Haiku vs Sonnet) based on task requirements, not preference
Understanding of guardrails, human-in-the-loop design, and failure handling

How We Evaluate You — Interview Process
Theory will not get you through. Expect:
Production walkthrough — show us a live or previously deployed agent. Walk us through the architecture, what failed, and how you resolved it
Cost breakdown — explain the token/compute cost of a system you built and how you optimized it
Live build task — given a business process, design an agent workflow on the spot

What Positive Looks Like
Agents running in produ

📌 Agentic Ai Engineer Bengaluru (India)
🏢 Substance
📍 India