AI Engineer

We’re building the first autonomous AI platform that can automatically detect, fix, and validate software vulnerabilities — end to end, at scale. Think of it as agents that can update dependencies, edit Dockerfiles, rebuild Go binaries with patched versions, and validate everything automatically. This is a deeply technical, research-driven role where you’ll design, implement, and scale AI agent systems that operate on real codebases. You’ll work at the intersection of backend engineering, AI systems, and application security — designing agents, context pipelines, and evaluation frameworks that bring autonomous reasoning to production.

• →Design and build AI agents from scratch to production — systems that detect, fix, and validate vulnerable components automatically
• →Develop and maintain infrastructure to support agent operations at scale [AIOps], including context management, evaluations and orchestration
• →Create agentic workflows that enable multiple agents to collaborate and reason jointly
• →Build tools and utilities that agents use (e.g., for image inspection, diff generation, static analysis)
• →Implement evaluation and performance measurement methods for agent reliability and accuracy
• →Develop hybrid and vector database applications for retrieval and context management
• →Build and integrate AI-related apps such as MCP-based systems, chat interfaces, and standalone agent utilities
• →Instrument all experiments with tracing, observability, and structured metrics for reproducibility

• 5+ years of hands-on experience in software engineering, preferably with exposure to AI-driven products or infrastructure
• Strong proficiency in Python for backend systems, tooling, and AI integration
• Solid foundation in software engineering, infrastructure, and cloud environments
• Proven experience working with LLMs and AI agents in applied settings
• Familiarity with LangGraph, LangChain, OpenAI, Claude Code, and Cursor frameworks
• Strong understanding of Docker and containerized development workflows
• Experience designing or orchestrating multi-agent systems or agentic workflows
• Awareness of context management techniques and prompt/tool/validation loop design

• Go experience, especially for rebuilding binaries or low-level utilities
• Experience with Argo, Kubernetes, or other orchestration systems
• Background in evaluation frameworks or agent performance measurement
• Experience with code-focused AI agents, developer tools, or AppSec/security automation
• Familiarity with vector databases, RAG pipelines, and graph-based context construction
• Understanding of DevSecOps, AppSec, or software supply chain security concepts