Ayoub Taoufik

Project 06 · Case study

AI recruitment workflow platform

Candidate and recruiter workflows with LLM-assisted profile and CV capabilities plus multi-source job-data synchronisation.

Freelance full-stack engineer · product delivery

For an AI recruitment platform, I built candidate and recruiter workflows around CV management, job matching, application tracking, interview scheduling, analytics, LLM-assisted profile and CV capabilities, and multi-source job-data synchronisation.

01 · Product context

Two sides of a recruitment workflow

Recruitment products have to serve people navigating opportunities and the teams working with candidate information. Candidate profiles, CVs, matching, applications, scheduling, and recruiter-facing workflows all need to remain connected.

The work combined that product surface with LLM-assisted capabilities and a multi-source job-data flow.

02 · Delivery scope

Product workflow and data-oriented engineering

I built the candidate and recruiter workflows, including CV management, job matching, application tracking, interview scheduling, and analytics. I integrated LLM-assisted profile and CV capabilities and multi-source job-data synchronisation.

The stack included Vue.js, NestJS, TypeScript, the OpenAI API, Elasticsearch, Redis, MongoDB, and Docker.

  1. 01 · Candidates

    From profile to application progress

    CV management, job matching, application tracking, and interview scheduling shaped a connected candidate journey.

  2. 02 · Recruiters

    Workflows built around information and decisions

    Recruiter-facing workflows and analytics were part of the same product surface as candidate information.

  3. 03 · AI and data

    Assistance within the product workflow

    LLM-assisted profile and CV capabilities were paired with multi-source job-data synchronisation and search-oriented platform components.

03 · What it adds to the portfolio

AI capability in a complete product workflow

This project complements the AI platform case with a different proof point: applying AI-assisted capabilities inside a concrete product workflow while working across frontend, backend, search, data, and delivery infrastructure.

Evidence treatment · contribution map

The parts of the product I worked on

Scope is deliberately grouped by my ownership boundary. It is not a claim about the whole system.

01

Built workflows for

  • Candidate profiles and CV management.
  • Job matching and application tracking.
  • Interview scheduling and analytics.
02

Integrated

  • LLM-assisted profile and CV capabilities.
  • Multi-source job-data synchronisation.
03

Used across the product

  • Vue.js, NestJS, TypeScript, Elasticsearch, Redis, MongoDB, Docker.