Case Studies

About Workopolis

Workopolis is Canada’s leading career site for job seekers and a leader in HR technology solutions for employers. Since 2000, the company has helped connect employers and candidates through exclusive partnerships and community sites, social networking, and mobile optimization.

Challenge

Workopolis needed to transition from a data-center–centric architecture to a cloud platform capable of supporting production-grade machine learning workloads. While internal data scientists were developing ML models, the existing environment lacked the tooling, packaging standards, and deployment patterns required to reliably train, version, and operationalize those models at scale.

Key challenges included containerizing ML training workflows, standardizing ML tooling and dependencies, and securely exposing model inference as cloud-native APIs. In parallel, legacy application services and APIs needed to be modernized using infrastructure-as-code and container-based patterns, while integrating seamlessly with proprietary ML models. All of this had to be delivered on AWS with strong security, scalability, and operational controls suitable for mission-critical, data-driven services.

Why Lucrodyne

Lucrodyne helps organizations in highly regulated and data-intensive industries turn AI initiatives into secure, production-ready systems. Our primary focus areas include insurance, healthcare, government, and industrial distribution, where reliability, governance, and operational scale are essential.

We specialize in cloud-native architecture, MLOps, and application modernization on AWS and Azure, working closely with internal data science and engineering teams to operationalize machine learning—from containerized training pipelines to securely hosted inference APIs integrated into mission-critical applications.

Lucrodyne has delivered solutions for enterprises and public-sector organizations, including Provincial Government engagements through a recognized Vendor of Record (VOR). This experience ensures we bring the security controls, compliance awareness, and delivery rigor required to deploy AI systems in regulated environments.

Our Services and Solutions

► Data and AI

► Cloud-Native Applications

► Cloud Infrastructure Services

Result

Workopolis successfully transitioned to a cloud-native AWS platform without material service disruption, enabling both application modernization and the operationalization of machine learning at scale. ML training workflows were containerized and standardized, allowing data scientists to iterate faster, reproduce experiments reliably, and promote models consistently across environments.

Secure, highly available inference APIs were deployed on AWS, providing a scalable and controlled way to serve ML predictions to core application services. Infrastructure-as-code and container-based deployment patterns improved release consistency, reduced operational risk, and ensured high availability across all backend services.

As a result, Workopolis established a robust MLOps foundation that bridged the gap between data science and production systems—accelerating time-to-model deployment, improving system resilience, and enabling AI features to be delivered confidently into production. In parallel, corporate services and analytics workloads were optimized through a hybrid architecture, balancing cloud scalability with cost-efficient colocation where appropriate.