• Partner with client executives and operators to map the current state of the business — how things run today, where the friction is, and what's most worth changing • Identify and prioritize the highest-leverage AI opportunities and frame the ROI in a way that drives decisions • Translate messy qualitative input into clear narratives, plans, and prioritized roadmaps • Work alongside our engineers to shape solutions — scoping what to build, informing architecture choices, and making sure execution stays tied to business outcomes • Present recommendations to C-suite stakeholders and keep business and technical stakeholders aligned throughout the engagement • Own the handoff to implementation so nothing gets lost in translation

• Develop and execute organic social media and community growth strategies across relevant platforms. • Build and grow engaged audiences on Reddit, Quora, LinkedIn, Telegram, Discord, X (Twitter), YouTube, and other channels. • Create, repurpose, and optimize content using AI-assisted tools and efficient content workflows. • Identify relevant online communities, conversations, trends, and opportunities to attract potential subscribers. • Drive meaningful engagement through community participation, discussions, comments, and direct audience interaction. • Develop and test organic and paid marketing campaigns to improve audience reach and subscriber acquisition. • Monitor marketing performance, track subscriber growth, and identify which channels and strategies are delivering results. • Experiment with new growth tactics, content formats, AI tools, and distribution channels. • Provide regular reporting and insights on campaign performance, growth metrics, and opportunities for improvement.

• Lead blueprint engagements: current-state assessment, gap analysis, and a prioritized, sequenced initiative roadmap. • Run executive discovery with C-Suite leadership and Boards to surface strategic goals, constraints, and success criteria. • Quantify the value: size the business problem, model TCO and ROI, and define the pilot, MVP, success criteria, and timeline. • Build board-ready transformation cases that connect AI investment to value-creation metrics: revenue per head, time-to-outcome, margin, and delivery predictability. • Facilitate executive alignment workshops that move stakeholders from awareness to decision. • Provide executive oversight across transformation programs, guiding AI Leads through strategic decisions, cross-workstream alignment, and evolving business priorities. • Serve as the executive advisor and escalation point through delivery close; act as the escalation point that unblocks technical and political issues. • Shape follow-on scope throughout delivery; by handoff you have a working hypothesis for the execution program and present it at readout. • Surface expansion signals across the client organization and route them to the account team. • Own and continuously evolve Climb's AI transformation methodology, including blueprint frameworks, value models, executive workshops, and delivery playbooks. • Mentor AI Leads on executive engagement, transformation strategy, value creation, and consultative leadership.

• Define and lead the strategy for data engineering, AI engineering and core data services to ensure scalability, efficiency and alignment with business objectives. • Oversee end-to-end management of data from multiple sources (IoT, field data, manufacturing and production systems), including engineering, modeling, deployment, monitoring and operations. • Lead global teams responsible for data architecture, data engineering, integration and data operations. • Define and enforce standards for software development, observability, resilience, service level indicators (SLIs), service level objectives (SLOs), incident management and operational procedures. • Ensure alignment of architectural and engineering practices with governance frameworks, domain models, security standards and platform stability requirements. • Take global ownership of AI platforms, including compute environments, MLOps toolsets, operational processes for AI and model hosting. • Ensure AI environments meet security, performance, availability, compliance and cost-optimization requirements. • Develop reusable components that facilitate development, deployment, monitoring and compliance of AI models. • Promote global convergence of data and AI capabilities, tools, engineering practices and operational processes. • Reduce fragmentation by standardizing development methods, monitoring structures, SLAs/SLOs and change management approaches. • Build reusable components, shared services and reproducible patterns to increase efficiency and reduce duplication. • Establish secure, governed self-service capabilities that enable teams to use data, analytics and AI autonomously. • Ensure compliance with export controls, international trade, data residency and regulatory requirements. • Eliminate redundant services and consolidate existing capabilities. • Ensure reliability, availability, observability and security of data and AI platforms. • Oversee global monitoring, incident and problem management, capacity planning, disaster recovery and lifecycle management. • Implement robust operational controls for access, change management, operational readiness and compliance. • Advance automation, Site Reliability Engineering (SRE) practices and continuous operational improvement. • Ensure developed capabilities effectively support analytics, artificial intelligence, digital transformation initiatives and operational workloads. • Collaborate with Data Science, AI, Ontology and Data Governance teams to ensure platforms align with modeling, metadata, traceability and data quality needs. • Support major enterprise programs including SAP S/4HANA, global data products, digital transformation, engineering modernization, production systems and support activities. • Lead global teams of engineers, architects, SRE specialists, platform operations professionals and data operations staff. • Foster a culture of technical excellence, operational discipline, innovation and continuous improvement. • Build organizational capabilities through workforce planning, skills development, mentoring and alignment of global teams. • Strengthen collaboration between engineering, operations and business partners.