Job Description & Details
This is a heavy-duty backend engineering gig focused entirely on AI infrastructure, LLM integrations, and modern agentic workflows. If you are tired of building standard CRUD apps and want to actually wire up Model Context Protocol (MCP) servers and orchestrate AI agents, this is where you want to be. You will be building the plumbing that allows large language models to talk safely and efficiently to enterprise databases and external APIs.
What You'll Actually Be Doing
Your day-to-day will be a mix of writing robust Python code and wrangling complex configuration files. You'll spend a lot of time building out REST APIs using FastAPI or Flask, designing YAML schemas for AI agents and workflows, and configuring MCP servers. It's not just about making things work, but making sure they scale, handle errors gracefully, and log properly when an LLM hallucination or API timeout brings down a pipeline. You'll also be working closely with the DevOps crew to push these agentic pipelines smoothly into cloud environments.
The Core Tech Stack
You need to know Python inside and out, specifically with modern frameworks like FastAPI for async backend work. Beyond standard web dev, the real litmus test here is your ability to handle LLM orchestration, prompt workflows, and tool calling. Experience with YAML-based configuration management and understanding how to design schemas for dynamic systems is non-negotiable. If you haven't touched MCP servers or structured tool integrations yet, you're going to need to spin some up quickly because that's the backbone of this stack.
Interview Expectations
Expect the engineering team to grill you on how you handle failure states when calling external LLMs or third-party APIs. They will likely ask a system design question like, 'How would you architect a fault-tolerant agentic workflow that retries failed tool calls without duplicating database writes?' They are secretly looking to see if you understand idempotency, rate limiting, and state management in non-deterministic systems. Another favorite will be testing your knowledge of async Python and how you prevent event loop blocking when dealing with slow network I/O from AI endpoints.
Application Advice
Don't just list Python and FastAPI on your resume and call it a day; the hiring managers are drowning in generic backend resumes. Make sure you explicitly highlight any project where you integrated LLMs, built custom APIs, or managed complex YAML/JSON configuration schemas for distributed systems. Pull keywords straight from the requirements—like 'Model Context Protocol', 'prompt workflows', 'FastAPI', and 'AI-agent orchestration'—and weave them directly into your project bullet points to get past the automated screeners.