• AI Products & Solution Architecture: • Design and guide implementation of AI-driven products, APIs, and platform features from concept to production; • Evaluate, select, and benchmark AI/ML models — including frontier LLMs, fine-tuned models, and open-source alternatives; • Architect scalable, observable, and cost-efficient AI systems that span experimentation, staging, and production; • Collaborate with product managers and business stakeholders to translate requirements into robust solution architectures; • Establish architectural standards for multi-agent systems, including context management strategies and memory designs. • Agentic AI & Process Automation: • Identify business processes that can be automated or enhanced via agentic AI, and define the architecture for doing so; • Design and oversee implementation of MCP server ecosystems that connect agents to enterprise data sources and tools; • Architect multi-agent workflows using orchestration frameworks (LangGraph, CrewAI, AutoGen), with appropriate human-in-the-loop checkpoints; • Integrate agent-to-agent communication standards (A2A, ACP) where multi-agent coordination is required; • Drive governance of MCP deployments: audit trails, authentication, rate limiting, and access control policies; • Embed AI into internal and external tools to improve operational efficiency across teams. • AI-Augmented Software Engineering: • Set up and continuously optimize AI-augmented developer environments (Claude Code, Cursor, GitHub Copilot); • Introduce AI into automated testing, deployment pipelines, code review, estimation, and technical documentation; • Define and enforce best practices for using AI coding tools safely, securely, and productively in software delivery; • Drive adoption of context engineering disciplines — designing prompts, tool schemas, and MCP resources that maximize agent reliability; • Governance, Security & Responsible AI: • Ensure all AI systems are designed with security-first principles: input validation, output guardrails, and least-privilege access; • Maintain AI compliance standards aligned with GDPR, the EU AI Act, EU Data Act, CRA, ISO27001, and internal model governance policies; • Implement observability and evaluation pipelines to detect hallucinations, drift, and performance degradation in production LLM systems.

• Conduct general and targeted product review and perform specified research consultation (both in-person and virtual) for a curated subscriber list of ~200+ accounts • Provide comprehensive customer service to existing users, including by not limited to: light technical troubleshooting, product access resolution, event and incident tracking and client-facing data reporting • Identify potential new users, new practice area opportunities and other elements of enhanced product adoption • Partner with sister products on the LexisNexis side, including collaboration for multi-product initiatives • Work closely with development teams to provide customer-centric levels of feedback regarding the design and implementation of product enhancements • Partner with the Sales Organization on retention strategies and best practices for the overall Customer Experience • Technical tracking of all activity via CRM tools • Adoption of workflow management tools including but not limited to: CoPilot, PowerAutomate, Salesforce, ChatGPT 5, Gong, and proprietary workflow solutions. • Cultivate and maintain exemplary working relationships with user base and ultimately support the Intelligize NPS and retention goals.

• Building and maintaining relationships with sales reps, partners, customers and other internal stakeholders • Gaining knowledge of HubSpot products for better-proposed solutions • Understanding customer challenges and how to deliver technical value to meet their business goals and objectives • Participating and directing projects relevant to presales, sales, and the wider business • Coaching and mentoring less experienced team members

• Engage with Solutions Architects to understand the full use case demand plan for prioritised customers • Lead the post-technical win technical account strategy and execution plan for the majority of Databricks use cases within our most strategic accounts • Be the accountable technical leader assigned to specific use cases and customer(s) across multiple selling teams and internal stakeholders, creating certainty from uncertainty and driving onboarding, enablement, success, go-live and healthy consumption of the workloads where the customer has made the decision to consume Databricks • Be the first contact for any technical issues or questions related to the production/go-live status of agreed-upon use cases within an account, oftentimes services multiple use cases within the largest and most complex organizations • Leverage both Shared Services, User Education, Onboarding/Technical Services, and Support resources, along with escalating to expert-level technical experts, to build the right tasks that are beyond your scope of activities or expertise • Create, own, and execute a point-of-view as to how key use cases can be accelerated into production, coordinating with Professional Services (PS) resources on the delivery of PS Engagement proposals • Navigate Databricks Product and Engineering teams for new product Innovations, private previews, and upgrade needs • Develop an execution plan that covers all activities of all customer-facing technical roles and teams to cover the below work streams: • Main use cases moving from ‘win’ to production • Enablement/user growth plan • Product adoption (strategy and activities to increase adoption of Databricks’ Lakehouse vision) • Organic needs for current investment (e.g. cloud cost control, tuning & optimization) • Executive and operational governance • Provide internal and external updates - KPI reporting on the status of usage and customer health, covering investment status, important risks, product adoption and use case progression - to your Technical GM