The Lead - AI/ML (Cybersecurity) will lead the design, development, and deployment of AI-driven solutions to improve cybersecurity efficiency, decision-making, and automation across the organization. The role focuses on identifying high-impact opportunities where AI, GenAI, and automation can reduce manual effort, enhance insight generation, and improve overall security outcomes through cross-team collaboration.
Key Responsibilities
- Strategy & Roadmap: Lead the end-to-end design and deployment of AI/ML and GenAI solutions; define a roadmap aligned with enterprise security and technology objectives.
- Solution Development: Apply Machine Learning (ML), Deep Learning (DL), GenAI, and Agentic AI to use cases such as security analysis, investigation assistance, and automated decision support.
- Lifecycle Management: Oversee the full model lifecycle, including validation, monitoring, performance optimization, and drift management (MLOps).
- Security Integration: Support the integration of AI solutions with existing platforms (SIEM, SOAR, VM) through APIs and microservices.
- Responsible AI: Ensure all solutions adhere to secure, explainable, and responsible AI practices, specifically addressing hallucination handling and privacy requirements.
- Leadership & Influence: Provide technical guidance to engineers and data scientists; communicate AI insights and limitations to senior stakeholders in business-relevant terms.
Technical Knowledge & Tooling
AI / Machine Learning / GenAI
- Core AI: Supervised/unsupervised learning, anomaly detection, and NLP.
- Advanced GenAI: Deep understanding of LLMs, Agentic AI patterns, and AI orchestration frameworks.
- Emerging Tech: Familiarity with Model Context Protocol (MCP) style integrations and modern hallucination-handling techniques.
- Operations: Awareness of MLOps, model explainability, and trust/ethics principles.
Cybersecurity Domain (Conceptual)
- Workflows: Working knowledge of SOC/Cyber Defense, Vulnerability Management, and Threat Intelligence.
- Tooling: Familiarity with SIEM, SOAR, and industry frameworks like MITRE ATT&CK.
Technology Stack (Indicative)
- Programming: Python or similar languages for data/AI workloads.
- AI-Assisted Dev: Experience using Claude, Cursor, or GitHub Copilot to accelerate prototyping and research.
- Infrastructure: Cloud platforms (AWS/Azure/GCP), SQL/NoSQL databases, and Data Lakes.
Experience & Qualifications
- Experience: 812 years of total experience with a strong track record in AI/ML, data, or advanced analytics.
- Production Success: Demonstrated experience delivering AI-enabled solutions in a live enterprise production environment.
- Leadership: Prior experience leading initiatives, mentoring team members, or influencing technical direction.
- Education: Bachelor's or Master's degree in Computer Science, Data Science, AI, or Cybersecurity.
- Certifications: AI/ML or Cybersecurity certifications are a plus but not mandatory.
