Allegis Group: AI/ML Meets Talent Solutions

The Challenge: Deploying AI/ML Models

AI/ML and MLOps—at Scale

Like most modern industries, data strategically fuels HR and talent management. Everything from record keeping to proactive risk prediction can be tracked and automated—if the right data strategy is in place. Allegis Group had a list of potential use cases to leverage AI/ML and generative AI to increase operational efficiency and overall productivity throughout the recruitment life cycle. The use cases included tools that could automatically update candidate profiles using information from email logs and keyword-based job description generation.

Allegis Group managed the experimentation process, building proof of concepts in-house with their own data scientists, but the question then became this: How can these solutions be operationalized at scale? Given TEKsystems’ expertise with Google’s AI/ML and generative AI product suite, we joined Allegis Group in a collaborative effort to extend this work to other operating companies. How? We helped establish best practices when it came to MLOps and leveraged Google Vertex AI to handle the deployment of AI/ML models.

If done right, this new Data Science Hub could be leveraged by all operating companies under Allegis Group. The platform would make use of Google Cloud’s growing toolkit of AI components and create a single source of truth for Allegis Group’s many streams of data.

Our Solution: AI/ML Solutions—Streamlined

Creating a Specialized Hub-and-Spoke Model

The buildout of a logical platform required reliability and scalability at its core. Allegis Group needed something next-gen, with enough resources to accommodate all aspects of a multifunctional data science team, from infrastructure to operations. Enter the “hub-and-spoke” model.

The goal? Encourage specialization while maintaining a solid foundation across all core aspects of Allegis Group’s data strategy—making it easier to change or replace one component without impacting the others.

Utilizing Google Cloud AI/ML services (Vertex AI, Gemini and AutoML), our team built a system for creating, supporting and operating various data science solutions. We used Vertex AI to create MLOps pipelines and Cloud Run to host APIs. We implemented security guardrails and constructed automated pipelines to streamline module onboarding and deployments.

Powerful Partnership: AI Use Cases and Business Impact

IDMO at the Core

Data science is a team sport that requires the combination of time, experience, effort, collaboration, data, talent and tools. In this case, infrastructure provisioning (I), data provisioning (D), mentoring (M) and machine learning operations (O) are at the hub of the model versus being disparate practices. Bringing these resources together ensures the adoption of successful models, which will inspire future experimentation.