Job Description & Details
This is a heavy-duty senior engineering role blending traditional full-stack architecture with cutting-edge Agentic AI systems. You aren't just building CRUD apps here; you're building scalable enterprise applications tightly coupled with autonomous AI agents and LLMs. If you've been dabbling in AI scripts on the side, this isn't it—they want someone who has production-level scars from deploying real-world agentic workflows.
What You'll Actually Be Doing
You'll spend your days architecting robust microservices and integrating complex multi-agent frameworks into enterprise systems. Expect to wire up frontend frameworks like React or Angular to high-throughput backend services written in Java, Spring Boot, or Python, while orchestrating AI loops using tools like LangGraph or CrewAI. You'll deal with real-world headaches like managing LLM latency, handling token limits, structuring state for autonomous agents, and ensuring your distributed systems don't fall apart under load.
The Core Tech Stack
You need a rock-solid foundation in backend engineering with Java/Spring Boot, Python, or Node.js, paired with modern frontend chops in React or Angular. On the AI side, practical experience building agentic workflows using LangGraph, CrewAI, or AutoGen over the last couple of years is non-negotiable. Familiarity with vector databases, RAG, containerization via Docker and Kubernetes, and cloud deployments will make or break your day-to-day survival here.
Interview Expectations
Expect the hiring team to grill you on how you handle state management and error recovery in multi-agent loops when an LLM hallucinates or drops a step mid-execution. They'll likely ask you to whiteboard a scenario where an autonomous agent workflow causes a bottleneck in a high-volume microservices architecture, and they'll want to see how you diagnose and optimize it. They aren't looking for textbook definitions; they want to know how you troubleshoot distributed async systems under pressure.
Application Advice
Make sure your resume screams 'Systems Architect with AI Execution' rather than just 'Prompt Engineer'. Explicitly highlight your 12+ years of full-stack delivery and weave in concrete examples of production deployments involving LangGraph, CrewAI, AutoGen, or vector databases. Ensure keywords like Java, Spring Boot, Python, React, Kubernetes, and Agentic AI are front and center so your profile easily sails through the ATS before hitting the hiring manager's desk.