Lead-AI/ML

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: 8–12 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.