It was 2 a .m. again. I had 10 tabs open. LinkedIn,
Glassdoor, my resume doc, a spreadsheet of roles
I was tracking, and my inbox full of, we regret
to inform your messages. And the glow of my screen
was the only light in the room. I remember thinking,
how is job hunting this, this, this hard, right?
I work with data for a living. I automate everything.
Did dashboards, reports, metrics, insights. But
here I was doing the most manual process of all.
Well, trying to get a job. And it wasn't just
about rejection. It was about inefficiency. Well,
every role that I've applied to, that I'm seeking
to apply to as well, they have a slightly different
task. Like every recruiter wants a slightly different
version of me. And I'm sure if you have applied
to jobs, you know what to do, right? Basically,
you have to tweak your resume for every role
that you're applying to. I realized that if I
wanted to survive this mentally and emotionally,
I needed a system. And since I built AI tools
for a living, well, I decided to build one for
myself. And that's how this all began. My experiment
to fix my job hunt with AI agents. Hey, I'm Mukundan
and this is Data and AI with Mukundan, the show
where we learn AI by building. In this episode,
I'm going to tell you the full story of how I
used three AI agents, my researcher, writer and
reviewer, to completely transform the way I applied
for jobs. We'll go behind the scenes into how
I built them, how they talked to each other and
how they saved me hours of stress every week.
But more importantly, I'll share how this process
reshaped my confidence, my clarity, and my career
direction. Because this isn't just some coding
project that I'm trying to do. It's a lesson
in self -automation, the kind of system that
saves us not just time, but also gives us sanity.
Well, at least it did for me. So, well, if you
ever look for a job, you know this feeling, right?
You wake up, you open whatever job site, say
LinkedIn. And suddenly your day disappears. You're
comparing roles, copy pasting bullet points,
adjusting titles, tweaking phrases like data
-driven insights for the umpteenth time. And
I was in that loop for months. Felt like it was
just tiring. It was just completely disheartening.
There were days I'd spend three hours on a cover
letter. And it's not just because I'm just sitting
and thinking of what to write, but mostly because
I'm procrastinating as well. Only to realize
also the application portal wanted a 250 character
summary instead. Or was the system a timeout
and everything I wrote was gone? Now, one night
I remember staring at a job description that
said, we're looking for a self -starter who can
work cross -functionally, leverage data to drive
growth and... communicate insights clearly i
thought that's literally every job description
and that's when the thought hit me why am i doing
this manually when i could use ai to do the repetitive
parts if i could automate data cleaning why not
automate resume tailoring if gpt can analyze
text i can't analyze job descriptions too and
the next day i decided to just build my own job
hunt system with Three roles. Meet these roles
now. Meet the agents whom I've assigned a role
for each of these agents. The first agent is
the researcher who reads the job post, extracts
the requirements and highlights key skills. The
writer agent uses my actual career history to
tailor a resume and a cover letter for that specific
job. The reviewer. It critiques both for tone,
clarity, and ATS readability. Now, together they'd
become my little AI team. Let me walk you through
how this works. First, I paste the job description
into my researcher agent. It scans it for signals,
not just keywords, but intent. For example, if
the posting says cross -functional collaboration,
five times. The agent infers that communication
and teamwork are key. If it mentions SQL, Tableau
or Looker, it classifies the stack. Now it outputs
a clean summary that looks like this. This role
focuses on driving business insights using SQL
and visualization tools. Collaboration and stakeholder
storytelling are central. The company emphasizes
experimentation and product growth. That's my
researcher agent doing its job, cutting through
the noise. And then the writer agent steps in,
takes my resume data, stored in structured JSON,
like job titles, accomplishments, metrics, and
rewrites key sections to match the tone and the
focus of the job. So instead of saying, let data
analysis for marketing campaigns. It becomes
collaborated with cross -functional teams to
drive experimentation insights, improving campaign
ROI by 23%. It's subtle, but it speaks the language
of the recruiter. Now, finally, the reviewer
agent takes both the job description and the
writer's output and scores everything for clarity,
impact, and ATS compatibility. It flags cliches.
It rewrites passive sentences, where it even
checks your formatting length. Now in 90 seconds,
I had what used to take me 90 minutes. So let's
look at part 3 where I'm turning this process
into a product. Once I had the agents working
individually, I wanted them to work together.
I wrote a small orchestration script, kind of
like a relay race. So when I paste a new job,
it triggers the researcher agent. Once the summary
is ready, it hands that off to the writer. When
the writer is done, the reviewer takes over.
At the end, I get three files. A tailored resume,
a personalized cover letter, and a short LinkedIn
summary. All ready to review. Or it was just
magic. But more than that, it changed how I approached
the job search emotionally. Before, every rejection
felt personal. Every application felt heavy.
It felt like data, like a test. Each rejection
became a new iteration for the AI. Now, I started
tracking patterns, which versions got callbacks,
which tone landed better, which skills got flagged
more often. Slowly, my AI system was learning
me. Now, let's learn about the psychology of
rejection. Let's take a pause as well. Because
this part really matters. Because job searching
isn't just about the skill. It's about stamina.
So when you've been rejected enough times, you
start internalizing it. Because you start thinking,
maybe I'm not just good enough. But here's what
the AI taught me. Rejection is just a data point.
It's not feedback on your worth. It's feedback
on your fit. I started to see my own job search
like a growth model. If a post doesn't convert,
it's not personal. It's a signal to tweak the
system. And suddenly it wasn't about hope anymore.
It was about iteration. AI gave me the distance
from the emotional chaos. It gave me back control.
Now let's see the human behind the code. But
here's the funny part, right? The more I automated
the process, the more human I felt. Because automation
didn't mean any detachment. It meant freedom.
Now, when my AI handled the grunt work, I had
time to actually research companies, to connect
with people, to write thoughtful follow -up messages.
to reflect on which roles truly aligned with
who I wanted to be. Now, in a strange way, my
AI system gave me back the emotional energy that
job searching had stolen. And somewhere in that
process, I stopped chasing roles and started
choosing them. And that's when I started seeing
results. Like interviews came in, conversations
became easier, because I wasn't coming from desperation.
I was coming from clarity. If there's one takeaway
from this episode, it's this. Automation should
never replace your purpose. It should remove
friction so that you can focus on it. My AI agents
didn't get me the job. They gave me back the
time and confidence to show up better. And they
reminded me that efficiency isn't about doing
more. It's about doing what matters. And if you're
listening to this right now, in the middle of
your own job search spiral, I see you. You're
not lazy. You're not even lost. You're just human
in a system that was never designed to make this
easy. Maybe it's time to build your own system.
Not a perfect one. Not a fancy one. Just one
that helps you show up. One that helps you stay
consistent, curious and calm. And if that system
includes AI, great. Because AI isn't the future
of job hunting, it's agency. And AI just helps
you reclaim it. The new kind of teamwork that
I want to talk about here is, this is the story
really. How I used three AI agents to fix my
job hunt. Not because I wanted shortcuts, but
because I needed structure. Like I said, AI didn't
find me the job. It helped me find myself again
in the process. And if you want to try building
your own version, I'll drop my own in the show
notes. And how, you know, it'll be like a job
hunt companion for you using AI. And it's the
same tool I used. Be free and open for anyone
who wants to make job searching a little less
soul crushing. I'll also be attaching a blog
on this link. So monitor this podcast in the
next week and you'll be getting a link to a blog
as well with the full architecture, prompt chains
and sample outputs, even resume examples. Until
then, keep learning, keep building and don't
let rejection define your direction. Just to
give some more clarity on when the blog will
be out. The plan is to release this episode on
November 4th. So it will be roughly a week from
November 4th. And if you're listening this, maybe
in 2026, just know that this is now about for
2025 that I will be releasing this episode. So
a week from November 4th is when you should expect
the blog post out. I'm Mukundan and this is Data
and AI with Mukundan. See you next week.