Location: Remote / Hybrid Type: Full-Time, Permanent Category: Artificial Intelligence / Software Engineering Experience Level: Mid-to-Senior
The Role
Are you a developer who lives at the intersection of Generative AI and Production Backend Systems? We are looking for a GPT & Backend Automation Specialist to join our team. This isn’t just a coding job; it’s about architecting the next generation of AI-driven workflows.
You will be responsible for building scalable backend systems, designing complex automation via n8n, and implementing cutting-edge RAG (Retrieval-Augmented Generation) architectures to make AI smarter and more contextual.
Technical Requirements
- Core Backend: Strong production-level experience with Node.js.
- Database Management: Proficiency in PostgreSQL (schema design, optimization, and indexing).
- AI Implementation: Hands-on experience with GPT APIs, embeddings, and Vector Databases.
- Automation: Expert-level knowledge of n8n or similar workflow automation tools.
- Architecture: Deep understanding of RAG architectures and similarity search.
- Environment: Comfortable in Linux server environments with a focus on scalable, clean code.
Preferred Skills (The “Bonus” List)
- Building autonomous AI Agents.
- Designing Multi-tenant architectures.
- Containerization with Docker and CI/CD deployment.
- Optimization of latency and cost in AI workflows.
What We Are Looking For
We need an “Ownership Mentality.” You aren’t just taking tickets; you are solving problems. You should be someone who stays up to date with the weekly changes in the AI landscape and knows how to apply them to production-ready systems.
Potential Interview Questions
Since this is a Mid-to-Senior role, the interview will move past “what is an API” and dive into architecture, cost, and latency.
1. Technical & Architecture Questions
- “Walk us through your RAG pipeline. How do you handle chunking and overlapping to ensure the LLM has enough context without hitting token limits?”
- What they’re looking for: An understanding of data preparation. Mention strategies like recursive character splitting or semantic chunking.
- “How do you mitigate ‘hallucinations’ in a production-level GPT application?”
- What they’re looking for: Discussing system prompts, temperature settings, and using “Grounding” (verifying AI output against the retrieved documents).
- “When designing a PostgreSQL schema for an AI-heavy backend, how do you handle high-frequency writes from automation logs?”
- What they’re looking for: Optimization skills. Mention indexing strategies, connection pooling in Node.js, or using a buffer/queue for logs.
2. Automation & Workflow (n8n)
- “How do you handle error-trapping and retries in a complex n8n workflow that involves multiple external APIs?”
- What they’re looking for: Reliability. Discuss “Error Trigger” nodes and how to prevent “infinite loops” in automated systems.
- “Describe a scenario where you had to choose between writing a custom Node.js function vs. using a pre-built n8n node.”
- What they’re looking for: Scalability vs. Speed. Custom code is often better for complex data transformation to save memory.
3. Scaling & AI Performance
- “LLM calls are expensive and slow. What strategies do you use to optimize latency and cost?”
- What they’re looking for: Efficiency. Mention Semantic Caching (storing results of previous similar queries) and choosing smaller models (like GPT-4o-mini) for simple tasks.
Career Advice for AI Specialists
1. Build a “Proof of Work” Portfolio
In AI, a certificate matters less than a working URL.
- Tip: Create a specialized AI Agent on GitHub that uses a Vector Database (like Pinecone or Weaviate). Show that you can handle the entire stack, from the ingestion script to the API response.
2. Master the “Embedding” Layer
Many developers just “plug in” an API. To be senior, you must understand how text becomes math.
- Tip: Learn about different embedding models. Understand the difference between cosine similarity and dot product search in your Vector DB.
3. Think “Product,” Not Just “Prompt”
A “Prompt Engineer” is a temporary role; an “AI Engineer” is a permanent one.
- Tip: Don’t just focus on the AI’s answer. Focus on how the AI integrates into the user’s workflow. This is why the job description asks for n8n and Node.js—they want someone who builds the “pipes” as well as the “brain.”
4. Stay “AI-Agile”
The tools you use today (GPT-4, n8n) might be replaced by faster/cheaper versions next year.
- Tip: Follow researchers and builders on X (Twitter) or LinkedIn. Being the first to implement a new “Agentic” framework can make you the most valuable person in the room.
Website Snippet: “Why this role?”
“The tech world is shifting from ‘Software as a Service’ to ‘AI as a Service.’ By mastering the backend automation stack (Node + PostgreSQL + GPT), you aren’t just getting a job; you are becoming an architect of the new economy.”