AI Engineer – Real-Time Fraud Intelligence

DataVisor; Anchorage (AK); On-site; USD 130,000 - 250,000; Full time

Benefits offered by this job

Base salary 130K–250K

Equity options

Medical, dental, vision coverage

Discretionary PTO and holidays

R&D opportunities

Team-building events

Job summary

DataVisor is seeking an AI Engineer to act as a technical architect for our Intelligence Layer and Data Consortium. This role focuses on building high-scale production services and real-time data pipelines that power fraud detection.

You will design distributed systems, implement AI applications with LLMs, and collaborate with Data Science to integrate ML models, rule engines, and label pipelines into production. Strong software fundamentals are essential; ML modeling is a plus but not required.

Qualifications

• 2+ years of production systems experience.
• Bachelor’s and Master’s in CS with ML focus.
• Distributed, cloud-native system design on AWS.
• Proficient in Python; other languages valued.

Responsibilities

• Design and operate distributed, production-grade services and data pipelines.
• Ingest real-time signals from millions of users with low latency.
• Collaborate with Data Science to deploy ML models and AI workflows.
• Migrate AI tooling to production-grade frameworks.
• Build AI applications using LLMs and rule engines.

Skills

Python

Distributed systems

Cross-functional collaboration

AWS

Java

Go/C++

ML concepts

Education

Bachelor’s and Master’s in Computer Science (ML focus)

Tools

Spark

Kafka

Flink

Docker

Kubernetes

ClickHouse

Job description

DataVisor is seeking an AI Engineer to act as a technical architect for our Intelligence Layer and Data Consortium. This role focuses on building high-scale production services and real-time data pipelines that power fraud detection.

You will design distributed systems, implement AI applications with LLMs, and collaborate with Data Science to integrate ML models, rule engines, and label pipelines into production. Strong software fundamentals are essential; ML modeling is a plus but not required.