AgenticAIEngineer(Fractional)

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: 1. **Production walkthrough** — show us a live or previously deployed agent. Walk us through the architecture, what failed, and how you resolved it 2. **Cost breakdown** — explain the token/compute cost of a system you built and how you optimized it 3. **Live build task** — given a business process, design an agent workflow on the spot **What Good Looks Like** * Agen