Data & AI Engineer

CYE is building a data-driven cybersecurity optimization SaaS platform that allows organizations to continually improve their cyber resilience. Using our platform, organizations can find, evaluate and fix the weakest links in their networks.

At CYE we understand that the best results are achieved by combining the power of AI with human expertise. That’s why we’ve built a world-class team of cybersecurity experts that augment the power of our platform.

We’re expanding our Data & AI group and looking for a passionate and skilled Data & AI Engineer to join our mission. This is a unique opportunity to work at the intersection of data engineering, machine learning, and cybersecurity innovation.

You'll work on

• Data Infrastructure & Engineering: Design, build, and scale production-grade data pipelines using Databricks and Spark. Ensure high levels of data integrity, system performance, and platform reliability
• AI & ML Collaboration: Collaborate with Data Scientists, Security Researchers, and Engineers to transform and deliver data for ML and GenAI model development. Build proof-of-concepts (POCs) to streamline operations such as model debugging, visualization, and experimentation
• Innovation & AI Systems: Prototype and integrate agentic workflows and LLM-powered systems (e.g., RAG, AWS Bedrock, LangGraph). Identify and develop intelligent, data-driven features that provide actionable insights to customers
  
  

• Deep understanding and hands-on experience with data lake architecture, real-time and batch processing
• Familiarity with tools like Spark, Kafka, Databricks, and SQL
• Experience designing systems powered by LLMs (e.g., OpenAI, Anthropic)
• Skilled in Retrieval-Augmented Generation (RAG), prompt engineering, embeddings, vector databases, and LLM orchestration frameworks (e.g., , LangGraph, OpenAI Agents SDK)
• Understanding of multi-agent design, memory, tool-use orchestration, and autonomous reasoning agents
• 3+ years of Python development experience in production environments
• Proficiency with Git, CI/CD practices, and deploying data/ML pipelines at scale
• Experience maintaining scalable, reliable ML/AI workflows in cloud-native environments
• Background in cybersecurity, threat intelligence, or exposure to security-focused data models - advantage
• Strong grasp of non-functional requirements: performance, reliability, scalability, observability. And awareness of COGS optimization and FinOps practices in SaaS data systems - advantage