• Own the vision, strategy, and roadmap for one of SimSpace's most strategic product portfolios. • Determine how Environmental Realism evolves to support emerging AI-native security products, autonomous cyber agents, and increasingly sophisticated cyber operations. • Serve as the cross-functional leader for Environmental Realism, bringing together engineering, UX, cyber experts, range content teams, customer-facing organizations, and executive leadership around a common strategy. • Drive alignment, facilitate difficult prioritization decisions, and ensure multiple teams execute as a coordinated portfolio rather than independent initiatives. • Partner directly with cybersecurity companies, enterprise security organizations, government agencies, and internal cyber experts to guide the evolution of capabilities including attack simulation, user activity generation, and AI-assisted environment creation. • Represent SimSpace's Environmental Realism strategy with customers, analysts, and industry partners. Translate insights into product strategy and roadmap investments.

• Leads the overall post-sale value realization strategy for Nexus Black. • Responsible for building, scaling, and managing the AI Adoption Engineering organization. • Set the vision, define operating models, influence GTM and product strategy. • Ensure customers achieve repeatable early wins, expanded value realization, and long-term strategic partnership. • Serve as executive sponsor for key enterprise customers. • Drive innovation in workflows, integrations, and AI-enabled customer processes.

• Design and implement LLM-powered pipelines, including prompt engineering, context management, and response synthesis • Build and optimize multi-agent orchestration systems where AI components interact, reason, and produce coherent outputs • Develop and maintain integrations with foundation model APIs (Anthropic, OpenAI, and others), managing latency, cost, and reliability at scale • Implement retrieval-augmented generation (RAG) and memory systems for persistent, context-aware behavior • Partner with the Evaluation/QA Engineer to build frameworks that measure output quality, coherence, and factual grounding • Collaborate with product and design to translate workflows into robust technical experiences • Ship production features across the stack, from the model layer to the application • Monitor, debug, and improve system performance, token efficiency, and guardrails

• Building production systems powered by software engineering, data science and modern AI. • Design, build and deploy production systems that combine software engineering, data science, large language models (LLMs), agentic AI and intelligent automation. • Work alongside Data Scientists, Engineers, IT teams and Business Analysts to transform ideas, models and business requirements into robust, scalable solutions. • Operationalising statistical and decision-science models, building AI-powered products and workflows, selecting and integrating foundation models, optimising cost and performance, and developing agent-based systems that automate and enhance complex business processes. • Success measured by the delivery of reliable, maintainable production systems that create measurable business value.