• Develop, own, and execute a comprehensive engagement strategy • Lead the content strategy and oversee creation of compelling content • Design and lead a scalable community-based program • Serve as a trusted advisor to senior leaders • Establish frameworks for tracking and measuring effectiveness of engagement initiatives • Provide guidance, coaching, and mentorship to junior team members • Partner with HR, Communications, Learning & Development, and business unit leaders

• Design and implement AI agents capable of reasoning, planning, and tool use • Build multi step agent workflows with memory and state management • Develop production ready RAG systems with optimized retrieval and context handling • Fine tune and evaluate LLMs for domain specific use cases • Implement guardrails, validation layers, and structured output controls • Design evaluation frameworks for reliability, hallucination mitigation, and safety • Build scalable REST APIs to expose AI capabilities • Integrate AI agents with enterprise tools and third party systems • Design microservices and asynchronous systems for high throughput workloads • Optimize inference latency, reliability, and cloud cost • Implement monitoring, logging, and observability for AI services • Work directly with clients to understand business requirements and translate them into AI solutions • Lead technical discovery sessions and solution workshops • Communicate complex AI concepts clearly to non technical stakeholders • Provide architectural recommendations and implementation roadmaps • Support pilots, proofs of concept, and production rollouts

• Define and execute a group-wide Agentic AI strategy across 30+ SaaS businesses, focused on deploying autonomous, decision-capable systems that drive measurable revenue growth and operational efficiency. • Design and operationalize production-grade agent architectures, including multi-agent systems, orchestration layers, tool use, memory frameworks, and secure LLM integrations. • Own ROI accountability for agent deployments, prioritizing high-impact use cases where autonomous agents meaningfully reduce cost, increase speed, or unlock new product value. • Establish scalable infrastructure standards, including data pipelines, evaluation frameworks, guardrails, monitoring, and MLOps for agent lifecycle management. • Embed agents directly into products and internal workflows, moving beyond copilots to systems that can execute, reason, and act within defined boundaries. • Create cross-portfolio leverage, developing repeatable agent frameworks that can be adapted across heterogeneous tech stacks and business models. • Build and scale a central Agentic AI capability, influencing CEOs and product leaders while shaping long-term competitive advantage at the group level.

• Own end-to-end delivery of AI red teaming engagements, from scoping through final report delivery. • Act as the primary customer point of contact during active engagements, ensuring clarity, momentum, and trust. • Partner with customers to define AI-specific testing objectives, threat models, and success criteria. • Structure red team assignments based on AI system architecture, model types, agent complexity, and engineer skillsets. • Attend and support engineers during customer kickoffs, technical deep dives, and executive report readouts. • QA all assessment reports before customer delivery, ensuring technical accuracy, clarity, and actionable findings. • Establish and maintain quality standards across deliverables, methodologies, and reporting templates. • Mentor and develop engineers on the red team, identifying skill gaps and creating structured development plans. Drive the evolution of AI red teaming methodologies based on real-world engagement learnings and emerging attack techniques. • Build scalable delivery processes that support growth, utilization tracking, and capacity planning. • Coordinate with Sales and Customer Success on pipeline visibility, forecasting, and engagement readiness.