AI / ML Engineering Intern

Job Description

AI Engineer – Intern | DataVisor

The Tone:
This is an internship at DataVisor. The company builds an AI-powered fraud and risk platform that delivers the best overall detection coverage in the industry. This role matters because interns will help construct the foundational intelligence layer and data consortium for DataVisor’s real-time fraud detection platform, gaining practical experience in how large-scale AI systems are built and deployed in production.

The TL;DR
• Role: Internship
• Pay: $25–$70 hourly
• Team: Works closely with experienced engineers and data scientists.
• Mission: Helps build the Intelligence Layer and Data Consortium that power DataVisor’s real-time fraud detection platform.
• Tech Stack: Spark, Kafka, Flink, OpenAI, Anthropic, Google (LLMs), LangChain, vector databases, AWS, Docker, Python

What You’ll Actually Do
• Data Engineering: Assist in building and maintaining high-throughput data pipelines.
• System Design: Learn to design and optimize backend systems that support large-scale, real-time decisioning.
• AI Application Development: Support the development of AI applications and agentic workflows using state-of-the-art LLMs.
• ML Operations: Help deploy and monitor pipelines for unsupervised and supervised ML models into real-time scoring APIs.
• Security Best Practices: Learn best practices for privacy-first system design, including tokenization and hashing to protect sensitive data.

The Must-Haves
• Background: Current Master’s or Ph.D. student in Computer Science, Machine Learning, AI, Data Science, or a related field.
• Experience: Familiarity with at least one of the following: distributed systems, machine learning, data engineering, or backend development.
• Skills: Strong programming skills in Python; understanding of core ML concepts (supervised / unsupervised learning).
• Bonus: Academic or project experience with big data frameworks (Spark, Kafka, Flink); coursework or project experience with LLMs, RAG architectures, LangChain, or vector databases; experience with cloud platforms (AWS) and containers (Docker); understanding of stream processing or real-time systems; interest in fraud, risk, or security domains.