• Partner with universities to solve evolving challenges • Engage directly with stakeholders to address shifting strategic goals. • Provide data- and AI-driven strategy that maximize long-term value. • Act as a trusted advisor and product expert • Build strong relationships with executives and operational leaders. • Develop deep product expertise to guide partners toward the right solutions. • Proactively monitor performance • Track partner outcomes against agreed goals and surface risks early. • Intervene with tailored strategies to ensure adoption and growth. • Configure and optimize AI agents • Reconfigure partner AI agents as goals and processes evolve. • Ensure agents are aligned with partner objectives and best practices. • Translate partner needs into scalable solutions • Collaborate with engineering to turn challenges into improvements. • Identify underutilization and re-engage partners with creative solutions

• Win customers — Run pilots, shape pricing, and validate retention + expansion • Build the product — Work with FutureSight’s product/engineering pod to launch • Raise capital — Lead the seed raise with our initial cheque • Build your team — Hire your founding team and set the cultural foundation**

• Assist in documenting customer GenAI use cases using established templates and value frameworks • Support customer discussions on AI value, readiness, and adoption under guidance from senior team members • Help gather and organize customer requirements, success criteria, and outcomes • Maintain structured documentation of approved use cases and adoption milestones • Support account teams with preparation of value narratives, summaries, and internal materials • Assist in organizing and preparing customer workshops, demos, and meetings • Capture field observations and feedback and escalate themes to senior stakeholders • Help maintain internal reference materials and best-practice documentation • Support tracking of AI initiatives, timelines, and deliverables • Maintain program artifacts including trackers, notes, and adoption summaries • Assist with coordination across AI Technical Specialists, Sales, and Customer Success teams • Ensure updates and deliverables are completed accurately and on time • Work closely with AI Technical Specialists to align business context with technical execution • Partner with Customer Success teams to support adoption activities and value tracking • Contribute structured inputs to Product and GTM teams through defined feedback channels

• Architect a comprehensive "Quality Intelligence" platform using generative AI to predict defect hotspots, intelligently optimize regression suites, auto-generate tests, and enable self-healing automation. • Define enterprise-wide AI-first testing strategy, including non-deterministic evaluation paradigms, continuous monitoring for drift/hallucination, and integration across the full SDLC. • Establish governance for ethical AI testing, aligning with emerging standards. • Design and implement advanced benchmarks, red teaming protocols, and adversarial testing for internal AI agents and generative features—focusing on hallucination rates, bias/fairness, prompt injection, jailbreaks, and goal misalignment. • Build evaluation pipelines with statistical rigor (e.g., multi-trial runs, LLM-as-judge, human-in-the-loop) using tools like LangFuse, LangSmith, DeepEval, RAGAS, or Arize Phoenix for metrics such as faithfulness, context precision, and safety compliance. • Partner with DevOps to embed AI-based testing into GitHub-based CI/CD pipelines (e.g., AI-generated tests, predictive flakiness detection, automated gating with quality signals). • Lead design of self-healing test frameworks (integrating AI plugins with Playwright/Cypress or similar) that adapt to UI/model changes with minimal maintenance. • Architect synthetic data generation, maintain golden data-sets and AI-powered data masking solutions to enable privacy-compliant, high-fidelity testing at scale. • Collaborate with product, data science, ML engineering, and security teams to influence AI feature design with quality guardrails from day one. • Evangelize and mentor: Upskill traditional QA engineers into AI-augmented testers through workshops, playbooks, and communities of practice. • Drive adoption of AI quality best practices organization-wide, including metrics dashboards for DORA + AI-specific indicators (e.g., hallucination rate, red team success rate, self-healing coverage).