About the job
About DataVisor
DataVisor stands at the forefront of the AI-powered Fraud and Risk Platform industry, delivering unparalleled detection coverage. Our open SaaS platform enables seamless data consolidation and enrichment, equipping organizations with real-time solutions for combating fraud and anti-money laundering (AML) activities. With our patented unsupervised machine learning technology, advanced device intelligence, and robust decision-making tools, DataVisor offers immediate performance enhancements. Our platform is designed for versatility, accommodating multiple use cases across diverse business units while significantly reducing total ownership costs compared to traditional solutions. Recognized as an industry leader, DataVisor serves numerous Fortune 500 companies worldwide.
Our award-winning technology is driven by a talented team of experts in big data, machine learning, security, and scalable infrastructure. We foster an open, positive, collaborative, and results-oriented culture. We invite you to be part of our journey!
Role Summary
We are on the lookout for enthusiastic, recently graduated or soon-to-graduate MS or Ph. D. candidates in Computer Science, Machine Learning, Data Science, or related disciplines to join our team as AI/ML Engineering Interns.
This internship provides a fantastic opportunity for candidates keen on understanding the construction and deployment of large-scale AI systems in production. You will collaborate with seasoned engineers and data scientists to contribute to the Intelligence Layer and Data Consortium that underpin DataVisor’s real-time fraud detection capabilities.
Key areas of focus include distributed systems, data pipelines, machine learning infrastructure, and applied AI, with exposure to agentic flows and large language models (LLMs).
What You’ll Do
- Data Engineering & Pipelines
- Assist in developing and maintaining high-throughput data pipelines utilizing technologies such as Spark, Kafka, or Flink.
- Support the processing and aggregation of real-time signals (e.g., device fingerprints, behavioral data) into shared intelligence systems.
- Distributed Systems & Scalability
- Learn to design and optimize backend systems that facilitate large-scale, real-time decision-making.
- Contribute to enhancing system performance, reliability, and latency during high transaction volumes.
- AI Applications & Agentic Flows
- Assist in developing AI applications and agentic workflows utilizing cutting-edge LLMs (e.g., OpenAI, Anthropic, Google).
- Explore natural language interfaces, intelligent rule suggestions, and automated strategy generation.
- Machine Learning Pipelines
- Help deploy and monitor pipelines for both unsupervised and supervised ML models.
- Contribute to the integration of models into real-time applications.
