Senior AI/ML Engineer

Job Responsibilities

• Lead and own the architecture and delivery of technical components of complex projects.
• Communicate effectively to align on requirements, execute on high-quality code, and collaborate with senior engineers and stakeholders throughout the development lifecycle.
• 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.
• Own evaluation & reliability from prompt libraries and fine-tuning to red-team testing, latency budgets, and fallback strategies.
• Lead cross-functional initiatives, partnering 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.
• Mentor junior engineers, foster engineering best practices, and contribute to architectural design reviews.

Required Skills & Qualifications

• 7+ years of experience in software development, engineering production systems using modern programming languages (Python, Go, Rust, or Java).
• Deep knowledge of agentic systems design, such as Centralized and/or Decentralized MAS (Multi-Agent Systems) architectures.
• Solid understanding of Graph structures and specifically graph databases.
• 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.
• Proven track record of gathering consensus and guiding multi-stakeholder initiatives through uncertain boundaries.
• 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.