Machine Learning Engineer

Rivago Infotech Inc; Dallas (TX); On-site; USD 120,000 - 170,000; Full time

Job summary

Rivago Infotech Inc. is seeking a Machine Learning Engineer to build and support production-grade fraud detection systems. The role focuses on real-time inference, feature engineering, APIs, and graph-based detection with deployment support.

Responsibilities include deploying fraud services, designing low-latency pipelines, and integrating models with REST APIs and microservices, while collaborating with MLOps and data teams.

Qualifications

• Hands-on Python with ML model deployment.
• Experience with APIs, microservices, and production ML systems.
• Knowledge of data pipelines, data engineering, and feature stores.
• Exposure to GCP, Databricks, Data Lake, or Data Warehouse platforms.

Responsibilities

• Build and deploy fraud detection services for production use.
• Develop low-latency inference solutions with a target of less than 250 ms.
• Design feature engineering pipelines for ML use cases.
• Integrate ML models with REST APIs and microservices.
• Support graph-based fraud detection using Neo4j.
• Improve scoring performance, reliability, and scalability.
• Work with MLOps teams for releases, monitoring, and production support.
• Support data quality, governance, and operational activities.

Skills

Python

ML Deployment

APIs & Microservices

GCP

Databricks

Neo4j Graph DB

Feature Engineering

Tools

REST APIs

Microservices

MLOps

Job description

Role: Machine Learning Engineer - Fraud Detection

Role Summar

yWe are looking for a Machine Learning Engineer to build and support production-grade fraud detection solutions. The role focuses on real-time inference, feature engineering, APIs, graph-based fraud detection, and production deployment support

Key Skill

• sMachine Learning Engineering and Real-Time Inferenc
• ePython, APIs, and Microservice
• sGCP and Databrick
• sNeo4j / Graph Databases and Feature Store
• sData Pipelines and Feature Engineerin
• gMLOps, Monitoring, and Production Suppor
• tAgentic AI Architecture (good to have

Responsibilitie

• sBuild and deploy fraud detection services for production use
• .Develop low-latency inference solutions with a target of less than 250 ms
• .Design feature engineering pipelines for ML use cases
• .Integrate ML models with REST APIs and microservices
• .Support graph-based fraud detection using Neo4j
• .Improve scoring performance, reliability, and scalability
• .Work with MLOps teams for releases, monitoring, and production support
• .Support data quality, governance, and operational activities

Required Qualification

• sHands-on experience in Python and ML model deployment
• .Experience with APIs, microservices, and production ML systems
• .Knowledge of data pipelines, data engineering, and feature stores
• .Exposure to GCP, Databricks, Data Lake, or Data Warehouse platforms