Case Study

Recommendation Engine

Category:

Recommendation Systems / Talent & HR Tech

Impact:

10 Weeks | 95% Cost Reduction

Background

Fullbound operates a hiring platform matching candidates to roles. Off-the-shelf recommendation systems lacked the domain-specific logic needed to match candidate skills, experience, and fit with job descriptions. They needed a custom engine that would optimize relevance, reduce costs, and scale smoothly with system demand.

Project Goals

Design a recommendation engine tailored to Fullbound's domain of candidate-job matching
Minimize computational and operational costs associated with recommendations
Develop and deploy a robust backend system to power the engine
Offer the service as a microservice, easily integrable into Fullbound's existing stack
Ensure scalability and maintainability on cloud infrastructure

Our Approach

Model & Algorithm Design

Created custom matching algorithms combining collaborative filtering, content-based features, and domain heuristics. Tuned the system to weigh candidate history, skills, role requirements, and engagement signals.

Cost & Infrastructure Optimization

Profiled and optimized compute pipelines to reduce resource usage. Applied indexing, caching, and batch inference to cut redundant computations.

Backend System Development

Built the full backend from scratch, including API endpoints, data ingestion, preprocessing, and scoring modules. Ensured modular architecture for flexibility and future extension.

Cloud Deployment & Integration

Packaged the recommendation engine as a microservice. Deployed on AWS, ensuring auto-scaling, reliability, logging, and monitoring. Integrated seamlessly into Fullbound's platform for real-time candidate suggestions.

Key Results

Delivered a custom recommendation engine tailored to candidate–role matching
Reduced recommendation costs by ~95% through infrastructure and algorithmic optimizations
Engine deployed as a microservice and fully integrated into the platform
Scalable architecture on AWS ready to handle growth

Technologies Used

Python

Scikit-learn / LightGBM / custom ranking models

Flask / FastAPI

Redis / ElasticSearch

AWS (EC2, Lambda, S3, Auto Scaling, CloudWatch)

Docker / Kubernetes