Build Your Own AI Agent to Search for Jobs: A Practical Guide
- Emmanuel White
- 1 hour ago
- 6 min read
Intro
This week, we asked on LinkedIn: who is already using AI agents to find their next job? The answers confirmed what we see daily in the market — a growing number of candidates are automating the search, and the gap between them and everyone else is widening.
The job market doesn't wait for you to refresh a page. By the time a role appears on a board, dozens of candidates have already applied — many through automated pipelines. The candidates who move first got the hedge. In this guide, we show you how to build a simple AI agent that searches for jobs on your behalf, filters them against your profile, and hands you a shortlist every morning. No coding degree required.
What is a job-search agent?
An AI agent is a tool that performs a task for you autonomously — in this case, monitoring the market and filtering opportunities. You define the criteria (role, seniority, industry, location, salary), and the agent does the repetitive work: scanning boards, reading job descriptions, scoring them against your profile, and reporting back what's actually worth your time.
Why build your own AI agent to search jobs?
Speed. Apply within hours of a role going live, not days.
Volume. An agent can scan hundreds of postings a day — no human can.
Filtering. It reads the fine print (budget, hybrid policy, visa sponsorship) before you do.
Consistency. It runs every day, even when you're busy or discouraged.
Step 1 — Define your target profile
Before building anything, write down what you're looking for in a machine-readable way:
Role titles and levels (e.g. "Senior Data Engineer", "Head of Finance")
Industries and company sizes
Location / hybrid / remote preferences
Salary range and key skills
Deal-breakers (e.g. no sponsorship, no on-call)
This profile is the "brain" your agent filters against. The sharper it is, the better the results.
Step 2 — Choose your tools
You don't need to write code. Practical options:
ChatGPT / Claude / Gemini with custom instructions — paste your profile, give it a daily routine, and let it draft searches and summaries
Job board alerts (LinkedIn, Indeed, Glassdoor) — set keyword alerts to feed your agent raw material
RSS feeds and aggregators where available
For the more technical: a simple script or an agent platform that checks boards and emails you a daily digest.
Option A — a working script, zero dependencies
Here is the script we use ourselves in this guide. It runs on any machine with Python 3 — no pip install — and uses a single feed: a web search over live LinkedIn job postings (Google-style queries, site:linkedin.com/jobs/view …). It needs one free API key from Brave Search (2,000 searches/month free — the same engine behind our own research), then it scores the results against your profile and prints the shortlist:
#!/usr/bin/env python3
"""job-agent.py - daily job shortlist via web search (LinkedIn jobs)."""
import json, os, urllib.parse, urllib.request
PROFILE = {
"roles": ["data analyst", "data engineer", "machine learning", "ai"],
"locations": ["singapore", "remote"],
"must_have": ["python", "sql"],
"dealbreakers": ["night shift"],
}
QUERIES = [
'site:linkedin.com/jobs/view "data analyst" remote',
'site:linkedin.com/jobs/view "data engineer" Singapore',
]
def search(query, count=20):
url = "https://api.search.brave.com/res/v1/web/search?" + urllib.parse.urlencode({"q": query, "count": count})
req = urllib.request.Request(url, headers={"Accept": "application/json", "X-Subscription-Token": os.environ["BRAVE_API_KEY"]})
data = json.loads(urllib.request.urlopen(req, timeout=15).read())
return data.get("web", {}).get("results", [])
def score(job):
text = (job.get("title", "") + " " + job.get("description", "")).lower()
if any(d in text for d in PROFILE["dealbreakers"]): return 0
if not any(l in text for l in PROFILE["locations"]): return 0
return sum(1 for r in PROFILE["roles"] if r in text) + sum(1 for m in PROFILE["must_have"] if m in text)
shortlist = []
for q in QUERIES:
for r in search(q):
shortlist.append((score(r), r))
shortlist.sort(key=lambda x: x[0], reverse=True)
shown = 0
for s, r in shortlist:
if s >= 1:
print(f"[{s}] {r.get('title','')}\n {r.get('url','')}")
shown += 1
if shown >= 10: break
print(f"\n--- {len(shortlist)} results scanned, {shown} shortlisted ---")
Run it: export BRAVE_API_KEY=*** && python3 job-agent.py → a scored shortlist of the top 10, every morning, in seconds. Tune the QUERIES list to your market — the syntax is exactly what you'd type on Google (site:linkedin.com/jobs/view "data analyst" remote, add -keyword to exclude, site:sg.linkedin.com/... for Singapore). (Tested live on 1 September 2026: 40 results scanned, 10 shortlisted — Randstad, Shopee, Singapore Airlines, GovTech and more.)
Option B — the no-code agent prompt
Prefer no code? Use this agent prompt in ChatGPT, Claude or Gemini instead — paste your profile into it once:
"Act as my job-search agent. Every morning, I'll send you the new postings from my LinkedIn/Indeed/Glassdoor alerts. Score each against this profile: {paste your profile}. Return a shortlist of the top 5 — each with a fit score (High/Medium/Low), the reasons in two bullets, and a suggested tailored opening line for the application. Never apply on my behalf; you prepare, I decide."
Step 3 — Automate the daily scan
Set a routine:
Morning: agent pulls new postings from your alerts/feeds
It reads each job description against your profile
It scores them (e.g. High fit / Medium / Low) and explains why
It emails you a shortlist with links and a suggested tailored application angle for the top 3
Step 4 — Make your applications human
Here's the trap: if your agent applies automatically with the same template, you look like everyone else — and recruiters can tell. Use the agent for search and preparation, but personalise every application:
Tailor the opening paragraph to the company and role
Reference something specific from the job description
Keep your CV clean and ATS-friendly
Step 5 — Use agents to prepare, not to replace you
The best use of an agent isn't applying — it's preparation:
Generate interview questions based on the job description
Draft a value proposition for the role
Research the company, its competitors and recent news
Practise answers to likely questions
Common mistakes to avoid
Blasting identical applications. Quality over quantity — always.
Ignoring the fine print. Let your agent flag budget and hybrid/remote details before you invest time.
Fake it. Never let an agent write a lie about your experience. It will surface in an interview.
Forgetting the human network. Agents find listed jobs; people find hidden ones. Use both.
Losing track of your applications. If a recruiter calls you, you must know exactly which role you applied for and when — no hesitation. Keep a simple tracker (role, company, date, status, key points you emphasised). If you're unsure on the call, be transparent and ask which role it's about — then deep-dive into that job description before calling back.
Our view at WeLinkTalent
We recruit senior and niche talent across Singapore and Asia — and we practise what we preach. Our own sourcing runs on AI agents that scan the market, score candidates and shortlist the top fits before a human consultant ever picks up the phone. The candidates who impress us most use AI the same way: as a force multiplier, not a substitute. They arrive prepared, informed and specific. That's the standard we help our candidates reach — and the reason a modern, tech-driven search firm is the partner you want in your corner.
FAQ — quick answers
Will an agent find me a job? It will find and filter opportunities; you still win the interview. No tool replaces preparation and fit.
Is this cheating? No — using AI to search and prepare is like using a spreadsheet. Passing off AI-written lies as your experience is not.
Do I need to be technical? No. Start with ChatGPT/Claude + job alerts.
What about confidential job hunting? Agents and boards can help you stay discreet — use private browsing and avoid public "open to work" signals if discretion matters.
Conclusion
The job search is changing. Candidates who build a simple agent gain speed, volume and clarity — and still bring the human judgement that closes the deal. Build your agent, but never stop being the reason they hire you.
This article responds to the conversation we started on LinkedIn — join it and tell us how your job-search agent is working for you.
An open series — share your agent
We're opening a space for the community: drop your best prompts, agent architectures and job-search scripts in our community/agent-scripts folder on GitHub — a folder per contributor, then a pull request. We'll feature the most effective ones here and on LinkedIn. The best agents win interviews; the shared ones win the market.
Comments