# AI recruiting at scale

Search, evaluation, integrations, and data systems built for recruiting workflows where quality and reliability both matter.

Author: Joshua Palamuttam
Published: 2026-06-01
Type: Case study
Role: Engineering and team leadership; Principal since October 2026
Period: 2021–present
Tags: applied-ai, search, distributed-systems
Canonical page: https://www.joshuapalamuttam.com/work/ai-recruiting-at-scale/

## The problem

Recruiting software combines human judgment, inconsistent data, search relevance, and
operational scale. A system can be technically correct and still be unhelpful. It can
also be clever and impossible to operate.

## My scope

Across my work at SeekOut, I have built and led systems spanning LLM-assisted candidate
evaluation, search, partner APIs, recruiting integrations, and the data systems that
support them.

I was promoted to Principal Software Engineer on October 1, 2026, and continue to
manage SeekOut's AI engineering team. The work here spans the engineering and
leadership roles I have held since joining in 2021.

## Selected outcomes

The work helped recruiters make sense of large candidate pools, supported dependable
partner integrations, and gave engineers a more practical way to use AI in their daily
work.

## What mattered

Across those systems, I kept coming back to the same things: clear contracts,
observable failure modes, evaluation grounded in real tasks, and architecture that the
next engineer could understand without a private tour.

Some implementation details remain private, so this case study focuses on the shape of
the problems, my role, and the public outcomes rather than internal architecture.