Caylent Services
Artificial Intelligence & MLOps
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Consolidating ML Infrastructure to Enhance Compliance and Innovation at Indeed
Artificial Intelligence & MLOps
Solution Implemented
→ Machine Learning Platform Consolidation: Migrated and consolidated 26 independent ML training implementations into two existing core platforms built on AWS infrastructure.
→ Standardization and Governance: Leveraged Amazon SageMaker, Lambda, S3, and IAM to standardize training, deployment, and cataloging, creating a unified governance framework.
→ Expert Partnership: Caylent provided end-to-end support, from co-developing the migration strategy to leading the re-implementation, integration, and testing for dozens of ML models and their training pipelines.
Outcomes Expected
→ Strategic Migration: Successfully migrated over 50 production ML models to the 2 platforms.
→ Accelerated Innovation: The unified platform allows Indeed's teams to rapidly iterate and deploy new ML-powered features by freeing them from the burden of maintaining fragmented infrastructure and reducing duplication of effort.
→ Enhanced Compliance: The standardized architecture provides a strong foundation for meeting the requirements of evolving AI regulations, such as the EU AI Act, and enables proactive observability of model performance.
Transforming the Job Search Journey with AI and AWS
As the world’s #1 job site¹, more people find jobs on Indeed than anywhere else. With over 615 million job seeker profiles worldwide² spanning 60 countries and 28 languages, and more than 3.3 million employers relying on its services, Indeed plays a critical role in shaping how the world finds work.
Indeed has long been at the forefront of addressing the complexities of the recruitment market, leveraging advanced algorithms and sophisticated AI models to power its services, detect fraudulent listings, and continuously improve the job search experience.
¹ Comscore, Total Visits, March 2025
² Indeed Data (Worldwide), Job Seeker Accounts With a Unique, Verified Email Address
Business Challenge
As a result of its rapid global growth and product innovation, Indeed’s AI infrastructure had become fragmented. While its core matching ML models were consolidated onto two optimized platforms, there were 24 additional ML training implementations supporting other features and analytical components, such as fraud detection. This complexity, while a natural outcome of rapid innovation, created operational overhead at this scale and made it challenging to apply unified governance and compliance standards across the board.
Maintaining compliance across dozens of different systems required significant specialized effort. This fragmentation also created inefficiencies that risked slowing the pace of innovation.
“To continue leading in a dynamic hiring landscape, we needed to evolve our underlying infrastructure,” said Chi-Chao Chang, VP of Engineering at Indeed. “Our goal was to build a unified platform strategy that could support Indeed’s scale, accelerate our delivery of new tools, and ensure robust governance and compliance for our AI systems with global regulations.”
Solution
Driving Efficiency and Compliance Through Consolidation
Indeed partnered with Caylent to consolidate its 26 independent ML implementations into two core platforms, built on top of AWS. The initiative was designed to create a more efficient, compliant, and scalable foundation for ML development without disrupting ongoing product innovation.
Leveraging a diverse set of AWS and Amazon SageMaker tools, AWS Lambda, Amazon S3, and AWS IAM, the new environment standardizes training, deployment, and monitoring practices. This consolidation has reduced duplicated effort and freed teams to focus on innovation.
“Caylent provided crucial acceleration for our ML platform consolidation, contributing to the migration strategy, migration of models and pipelines, testing, and deployment,” said Chang. “The Caylent team executed the migration of over 50 machine learning models across four complexity-based cohorts, working closely with our product and engineering teams to ensure a seamless integration with our two core AWS-based platforms.”
The result is a standardized ecosystem that ensures consistent deployment practices, unified observability, and stronger compliance tracking across all models. This drives up efficiency and reduces maintenance overhead for product teams, empowering them to build the next generation of features for Indeed.
A Partnership Focused on Empowering Teams
Caylent’s expertise was instrumental to the success of this migration. “Caylent has proven to be an invaluable and trusted partner. Their empathetic approach and best-in-class skills were evident as they seamlessly integrated across multiple Indeed teams,” said Chang. “They worked in a dynamic and proactive manner, readily incorporating feedback to continuously enhance the quality of their deliverables and providing critical recommendations to help us overcome multiple challenges.”
For end-users, this foundational work empowers Indeed’s teams to innovate more rapidly. By streamlining the underlying infrastructure, Indeed can accelerate its mission of helping people get jobs. The increased development velocity means new tools to aid job seekers and employers can be delivered faster and more efficiently than ever before, with compliance built in across the two platforms.
“This project was ambitious and spanned several teams across the organization,” said Chang. “The challenge was ensuring business continuity while increasing efficiency for our product teams—all without disrupting their roadmaps. The Caylent team’s expertise, flexibility, and collaboration were critical in making this possible.”
Technologies Used
The consolidated environment was designed around a core of AWS services. Amazon SageMaker was used for standardized model training and deployment on one of the two core platforms. Amazon S3 provided scalable and secure storage for large datasets and model artifacts across the ecosystem. AWS Lambda was leveraged for serverless orchestration of data processing and model training workflows, while AWS IAM delivered the robust security controls needed to manage access and permissions across all teams and services.
