I built a custom LLM agent using Grok to automate my job search process

Searching for a new role in this market feels like a full-time job itself, and manually scrolling LinkedIn for “remote” openings is draining. After two consecutive layoffs I decided to stop resisting the tools and start leveraging them. I created a dedicated “Job Hunt” agent powered by Grok to manage the entire pipeline—from discovering listings to preparing for interviews.

The setup wasn’t a complex coding project; I simply initialized a custom bot and gave it a clear persona. I didn’t just say “find me a job.” I granted it permission to scrape my public digital footprint to build a baseline profile.

If you want to replicate this AI workflow, don’t feed it a static PDF resume. A resume is a curated lie; your public trail is the real signal. Here is the prompt strategy I used to start:

Look at my online presence and find all the details you know about me. 
Use this to help me find my next role. 
Ask me any specific questions you need to refine the search.

If you lack a large public footprint like mine, you should manually provide your LinkedIn URL or a detailed bio. The aim is to shift from generic keyword matching to semantic understanding of your career trajectory.

What impressed me was the model’s ability to synthesize my history. It didn’t just list my jobs; it identified a “through line.” It saw my experience as a teacher, my work with the Playwright community at Microsoft, and my current role as a platform engineer on Zephyr's AI platform. It correctly identified my niche: teaching people how to build with MCP and agents while actually shipping the workflows.

Even more striking was its ability to uncover hidden preferences. It dug through my old posts and found a specific mention from March where I labeled an Anthropic role as my “ideal” position—with the caveat that I needed it to be remote due to family constraints. It connected that dot automatically. It wasn’t merely searching for “Platform Engineer”; it was searching for “the role I described as perfect three months ago.”

I’ve been running this as a continuous monitoring task. Instead of checking job boards each morning, I just ask the agent for a status update.

• Search Parameters: I set it to prioritize Cursor and Grok Bot, then expand to broader AI agent and education roles.
• Constraint Filtering: It automatically filters for “Spain/Remote” and flags US-based roles that might require an office exception.
• Aggregated Sourcing: It pulls from multiple job boards and company career pages simultaneously, saving me hours of manual clicking.

The results have been highly relevant. It suggested roles such as Anthropic Technical Enablement Lead and DevRel positions at Databricks. It even offered a qualitative opinion: “If I were picking one exception to test, I would try Anthropic or Databricks.”

The true power of this LLM agent emerges when you move from “finding” to “digging.” Once it identifies a lead, I don’t just glance at the title. I ask it to pull the full Job Description (JD) and analyze it.

I can command the agent to:

1. Deconstruct the JD: Break down the specific technical requirements and soft skills.
2. Simulate Interviews: Use the JD and my profile to run mock interview sessions.
3. Gap Analysis: Identify where my current experience might fall short of the role's requirements so I can prepare specific talking points.

This isn’t merely automation; it’s about having a senior-level researcher working for you 24/7. If you’re currently in the middle of a job hunt, stop treating LLMs like a search engine and start treating them like a specialized agent.