Principal AI Engineer

Job Responsibilities

• Design & build the AI layer that powers autonomous detection, RAG-backed investigation, and auto-remediation workflows.
• Develop and productionize large-scale LLMs, graph-based reasoning engines, and streaming feature pipelines that operate on billions of security events.
• Evaluate & reliability—from prompt libraries and fine tuning to red-team testing, latency budgets, and fallback strategies.
• Mentor & grow a cohort of AI engineers; run design reviews, uphold code quality, and instill a security-first mindset.
• Partner tightly with Product, Detection Engineering, and Customer Success to translate real-world attacker behavior into robust ML and rule-based detections.
• Experiment with retrieval-augmented generation, tool-calling agents, and multi-modal models (text + logs + graphs) to keep defenders decisively ahead.

Required Skills & Qualifications

• Software Engineering & Architecture Expertise: 10+ years of experience in software development, engineering production systems using modern programming languages (Python, Go, Rust, or Java).
• Deep knowledge of LLM architecture, prompt engineering, and Vector database workflows.
• Hands-on experience building agents, orchestration frameworks (LangChain/LangGraph, Agno AGI, or custom), and evaluation harnesses.
• Deep understanding of microservices architecture, containerization (Docker, Kubernetes), and event-driven systems.
• Strong fundamentals in API design (REST/gRPC) and distributed systems.
• Clear, concise communication skills and a bias for collaborative problem-solving.

Nice-to-have

• Prior work in cybersecurity (SIEM, EDR, SOAR, or MDR).
• Experience with graph databases or security-focused knowledge graphs.
• Familiarity with cloud infrastructure security (AWS, GCP, or Azure).
• Background leading teams in high-growth startups or enterprise SaaS.

Soft Skills

• Strong problem-solving and analytical skills.
• Excellent communication and leadership abilities.
• Ability to mentor and influence engineering teams.
• Passion for cybersecurity and automation.

Education & Certifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
• Relevant certifications (AWS/GCP Professional Engineer, Kubernetes, or security-related credentials) are a plus.