AI Applications Engineer - AI Integration

Fully Remote • Product Development

About the Company

Our company works with organizations to promote data protection best practices within their organization as well as establish compliance with standards such as PCI DSS and regulations such as the European General Data Protection Regulation (GDPR).More than 225,000 users in 100+ countries look to our company for tools, technology, and strategic security support. Founded in 2008 with a decade of service to the hospitality industry, we are a privately held company with regional offices across the United States and internationally. We are currently seeking an AI Applications Engineer - AI Integration to add to our team.

Accountability Summary

As an AI Applications Engineer - AI Integration, you will build the next generation of AI-powered applications for cybersecurity and compliance intelligence. This will include designing and developing backend services that aggregate data across multiple systems, integrate AI capabilities, and deliver predictive insights to enterprise customers.You will work on a multi-tenant SaaS platform that supports organizations with complex hierarchies and high data demands. Your role will include designing APIs, building AI-powered data processing pipelines, and optimizing large-scale database operations to enable real-time business intelligence.

Duties and Responsibilities

The AI Applications Engineer - AI Integration will be responsible for the following tasks:

• Design and build REST APIs in Node.js and PHP to deliver dashboard data and AI-driven insights.
• Integrate LLM APIs (e.g., OpenAI, Anthropic, Cohere, Google Gemini, Azure OpenAI) for natural language recommendations, reporting, and risk analysis.
• Ability to quickly create Proof-of-Concept code and engage with multiple stakeholders to drive PoC productization.
• Develop Python microservices with pandas, numpy for preprocessing, analytics, and statistical modeling.
• Implement real-time data aggregation pipelines from existing SQL databases.
• Design and optimize database schemas for multi-tenant SaaS and time-series data storage.
• Build serverless functions (AWS Lambda) for asynchronous AI processing and pipeline orchestration.
• Implement event-driven workflows using SQS, EventBridge, or equivalent messaging systems.
• Develop algorithms for pattern recognition, anomaly detection, and trend analysis.
• Create predictive models for forecasting and risk scoring.
• Implement caching strategies (e.g., Redis) for dashboard performance optimization.