AI/ML Engineer

Job Responsibilities

• Project Execution: Ability to drive and land large abstract projects from start to finish. This means communicating effectively to gather consensus, efficient delegation, and bringing multiple stakeholders on the journey.
• AI Layer Engineering: Design & build the AI layer that powers autonomous detection, RAG-backed investigation, and auto-remediation workflows.
• Productionize Reasoning Engines: Develop and productionize large-scale LLMs, graph-based reasoning engines, and streaming feature pipelines that operate on billions of security events.
• Evaluation & Reliability: Own evaluation & reliability-from prompt libraries and fine-tuning to red-team testing, latency budgets, and fallback strategies.
• Cross-Functional Collaboration: Partner tightly with Product, Detection Engineering, and Customer Success to translate real-world attacker behavior into robust ML and rule-based detections.
• Push the Frontier: 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

• Core Engineering: 1 - 3+ years of experience in software development, engineering production systems using modern programming languages (Python, Go, Rust, or Java).
• Agentic Systems: Deep knowledge of agentic systems design, such as Centralized and/or Decentralized MAS (Multi-Agent Systems) architectures.
• Graph Architectures: Solid understanding of Graph structures and specifically graph databases.
• Orchestration Frameworks: Hands-on experience building agents, orchestration frameworks (LangChain/LangGraph, Agno AGI, or custom), and evaluation harnesses.
• Distributed Systems: Deep understanding of microservices architecture, containerization (Docker, Kubernetes), and event-driven systems.
• APIs: Strong fundamentals in API design (REST/gRPC) and distributed systems.

Soft Skills

• Communication: Clear, concise communication skills and a bias for collaborative problem-solving.
• Leadership Alignment: Proven track record of gathering consensus and guiding multi-stakeholder initiatives through uncertain boundaries.
• Analytical Rigor: Strong problem-solving and analytical skills.

Nice-to-have

• Domain Background: Prior work in cybersecurity (SIEM, EDR, SOAR, or MDR).
• Startup Mentality: Background driving high-impact engineering initiatives in high-growth startups or enterprise SaaS.
• Cloud Infrastructure: Familiarity with cloud infrastructure security (AWS, GCP, or Azure).

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.