Cye is building a data-driven cybersecurity optimization SaaS platform that helps organizations
continuously improve their cyber resilience. With Cye, organizations can identify, evaluate, and remediate
the weakest links in their networks.
At Cye, we believe the best results come from combining the power of AI with deep human expertise. That’s
why we’ve built a world-class team of cybersecurity experts who augment and enhance the capabilities of
our platform.
We’re expanding our Data & AI group and looking for a passionate, experienced Senior Data & AI Engineer
to join our mission. This is a unique opportunity to work at the intersection of data engineering, LLMpowered systems, agentic workflows, and cybersecurity innovation
What you’ll work on
- Data Infrastructure & Engineering
Design, build, and scale production-grade data pipelines using Databricks, Spark, and modern cloud-native
technologies. Ensure high standards of data integrity, system performance, reliability, and scalability.
- Core Backend & Platform
Design and contribute to scalable backend services and platform capabilities using microservices and
event-driven architectures. Build reliable APIs, integrations, and asynchronous data flows that support highscale AI, data, and cybersecurity use cases.
- LLM & Agentic Systems
Design, prototype, and integrate LLM-powered systems, including Retrieval-Augmented Generation
pipelines, agentic workflows, tool-using agents, multi-step reasoning flows, and AI-driven automation. Work
with technologies such as AWS Bedrock, OpenAI, Anthropic, LangGraph, vector databases, and modern
orchestration frameworks.
- AI-Assisted Engineering & Developer Productivity
Explore and apply advanced AI coding assistants and software-engineering agents, such as Codex and
Claude Code, to improve development velocity, code quality, debugging, testing, and experimentation.
Build proof-of-concepts and internal tools that help engineering and research teams work more effectively
with AI-powered development workflows.
- Intelligent Cybersecurity Features
Collaborate with Security Researchers, Engineers, and Product teams to identify opportunities for
intelligent, data-driven features that deliver actionable cybersecurity insights to customers. Transform
complex cybersecurity and platform data into reliable, explainable, and useful AI-powered capabilities.
You’ll be a great fit if you have
- Deep understanding and hands-on experience with data lake architectures, batch processing, and
real-time data processing.
- Experience with tools and technologies such as Spark, Kafka, Databricks, and SQL.
- Hands-on experience designing and building LLM-powered systems using providers such as OpenAI,
Anthropic, AWS Bedrock, or similar platforms.
- Strong practical experience with Retrieval-Augmented Generation, embeddings, vector databases,
prompt engineering, evaluation techniques, and LLM orchestration frameworks such as LangGraph
or OpenAI Agents SDK.
- Understanding of agentic system design, including tool use, memory, planning, multi-agent
collaboration, and autonomous reasoning workflows.
- Experience working with advanced AI code assistants and coding agents, such as Codex, Claude
Code, or similar AI-native development tools, to improve engineering productivity.
- 3+ years of Python development experience in production environments.
Proficiency with Git, CI/CD practices, and deploying data, automation, or AI-powered pipelines at
scale.
- Experience maintaining scalable, reliable AI/LLM workflows in cloud-native environments.
- Strong understanding of non-functional requirements, including performance, reliability, scalability,
observability, security, and cost efficiency.
Advantages
- Background in cybersecurity, threat intelligence, or security-focused data models.
- Experience building internal developer-productivity tools, evaluation harnesses, or AI-assisted
engineering workflows.
- Awareness of COGS optimization and FinOps practices in SaaS data and AI systems.
About us
Cye helps security and risk leaders gain a clear, defensible view of their cyber exposure, grounded in financial impact and real-world attack paths. By continuously quantifying exposure and validating it in context, organizations can establish a strong baseline, prioritize decisions with confidence, and track measurable reduction over time. 
