KEY RESPONSIBILITIES
1. AI Vision and Enterprise Strategy
- Define TAC's multi-year AI roadmap aligned with the 2030 Vision.
- Identify high-impact AI opportunities across vulnerability management, application security, SOC 2 automation, IoT security, and Web3 audits.
- Establish TAC's AI leadership position relative to global cybersecurity competitors.
2. AI-Driven Product Innovation
- Lead development of AI-powered cybersecurity capabilities including predictive vulnerability detection, automated remediation, risk scoring, agentic AI workflows, and generative AI for cyber defense.
- Convert prototypes into scalable, revenue-generating enterprise features.
- Collaborate with product and engineering to integrate AI deeply into the ESOF ecosystem.
3. AI Architecture, Data and Infrastructure
- Build TAC's end-to-end AI platform including data lakes, pipelines, feature stores, and model-inference systems.
- Ensure distributed, secure, and scalable AI deployment across global customer environments.
- Implement robust ML Ops and production monitoring systems.
4. Governance, Security and Responsible AI
- Establish TAC's standards for responsible AI, including model explainability, bias mitigation, privacy, and regulatory compliance.
- Build resilient, enterprise-grade AI systems capable of withstanding adversarial attacks.
- Ensure alignment with global frameworks (ISO, SOC 2, NIST, GDPR, etc.).
5. Leadership, Culture and Talent
- Build and lead a world-class AI organization across ML engineering, data science, infrastructure, and research.
- Drive AI literacy and adoption across the entire company.
- Serve as TAC's global spokesperson and internal champion for AI transformation.
6. Partnerships and Ecosystem Expansion
- Develop partnerships with AI labs, cloud hyperscalers, academic institutions, and cybersecurity research bodies.
- Represent TAC at global cybersecurity and AI conferences, industry panels, and policy forums.
- Build TAC's external reputation as a pioneer in AI-driven cybersecurity.
IDEAL CANDIDATE PROFILE
Experience
- 5-7 years in AI/ML, with at least 5 years in senior leadership.
- Proven track record deploying AI at scale in cybersecurity, enterprise SaaS, cloud, or regulated sectors.
- Built or led AI teams serving enterprise or government clients.
Technical Expertise
- Generative AI, LLMs, deep learning.
- ML Ops, model governance, inference optimization.
- AI security (adversarial ML, data protection).
- Large-scale data engineering and distributed systems.
Leadership Competencies
- Strategic thinker with strong product and business acumen.
- Exceptional communication with ability to work with Board, C-suite, enterprise clients, and engineering teams.
- Thrives in fast-paced, founder-led, high-growth environments.
- Experience working across global markets (US, EU, Middle East, APAC).
SUCCESS METRICS (KPIs)
- Percentage of TAC products with AI-enabled capabilities.
- Revenue contribution from AI-powered features.
- Reduction in manual processes through automation.
- Accuracy, robustness and security of deployed AI models.
- Speed from prototype to production.
- Team growth, productivity and innovation output.
- Partnerships, patents, awards, and industry recognition.
