Bemobi processes recurring payments for telecom, utilities, education, and fintech clients across 60 countries, with particular strength in Latin America. The tech stack is modern and distributed (Go, Java, Kafka, AWS microservices) with active adoption of ML tooling (SageMaker, MLflow) — a sign they're moving fraud detection and risk scoring from rules-based systems into predictive pipelines. Hiring velocity is accelerating, skewed toward engineering and data roles, which aligns with their current project portfolio: fraud prevention, MLOps infrastructure, and a new data studio platform.
Notable leadership hires: Tech Lead, Machine Learning Lead
Bemobi is a payment infrastructure specialist built for subscription and recurring-revenue businesses. They process payments in 60 countries across telecom, utilities, education, and internet provider verticals, with particular operational scale in Brazil and Chile. The company serves 1,500 clients and processes billions in annual transaction volume. Core operations span payment processing, fraud prevention, compliance, and lately, internal analytics and ML-driven risk models. They were founded in 2009 and have scaled to 501–1,000 employees while maintaining Great Place to Work certification.
Go, Java, Python on AWS (ECS, Lambda, RDS, DynamoDB, SQS). Message broker: Kafka. APIs: GraphQL, gRPC. DevOps: Docker, Terraform. CI/CD: Bitbucket, Git. ML: SageMaker, MLflow (adopting).
Fraud prevention (risk scoring models), MLOps infrastructure (SageMaker/MLflow), a data studio platform, HR analytics dashboards, and HR chatbot. Also working on integration documentation and post-launch validation.
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