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
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 Good Looks Like
Agents running in production, not in slides
Clear ROI per agent — time saved, cost reduced, error rate dropped
Builds lean — chooses the cheapest model that does the job reliably, not the most impressive one
What Will Get You Rejected
Portfolio of tutorials and Jupyter notebooks with no deployment history
Can explain LangChain architecture but never shipped one to production
Optimizes for technical elegance over business outcome
What You Won't Be Doing
Academic research or model training
Waiting for perfect requirements — expected to drive clarity from ambiguous briefs
Building without a business case
Remote working
At Substance, we’re all about action, not just talk. If your profile aligns with what we need, you’ll hear from us within 1-2 weeks. If not, no fluff—just know we value your interest and will keep you in mind for future roles where your skills can make an impact. We focus on real connections and meaningful matches, so when the right opportunity comes, we’ll be ready to make it happen.
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