ML ops and credit risk analytics platform for financial services
Georgia IT has shifted from traditional IT consulting into machine-learning infrastructure and financial analytics. The stack reveals a modern data science operation—Python, TensorFlow, PyTorch, Kubernetes, Kafka, BigQuery, Databricks—with heavy investment in MLOps tooling (Airflow, MLflow, Kubeflow) and vector databases (Pinecone, Milvus, Weaviate). Active projects cluster around fraud detection, credit scoring, and predictive underwriting, while pain points center on scaling ML infrastructure and governance. Hiring velocity is accelerating with five senior-level roles open, weighted heavily toward data (80%), suggesting a transition from IT services delivery into a data-product business.
Georgia IT, founded in 2007 and based in Alpharetta, GA, is a 51–200 person IT services and consulting firm that has evolved into financial-services technology. Beyond legacy IT consulting—network design, virtualization, disaster recovery—the company now builds machine-learning pipelines and risk-analytics products for credit and fraud use cases. Core competencies include MLOps automation, data governance, and predictive modeling on cloud infrastructure (GCP, Databricks). The company is replacing legacy relational databases (Oracle, MySQL) with modern data warehousing and NoSQL approaches, reflecting a move away from monolithic IT infrastructure work toward AI-driven product development.
Python, Java, TensorFlow, PyTorch, Kubernetes, Docker, Kafka, BigQuery, Databricks, Apache Airflow, MLflow, Pinecale, Milvus, Google Cloud Platform, and Looker. They are migrating from Oracle Database and MySQL.
MLOps pipeline automation, AI model integration, fraud detection, credit risk modeling, predictive underwriting dashboards, and data governance compliance for financial products.
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