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Vice President, AI & Data Strategy

• Build and evolve TENEX's internal AI platform: a reusable foundation of tools, custom skills, Claude Projects, integrations, and department-specific specialists that scale AI impact across every team.
• Partner with teams across TENEX (Sales, Customer Success, Marketing, Finance, Legal, HR, Detection Engineering, Security Operations, Forward-Deployed Engineering) to surface needs that shape our internal AI platform roadmap.
• Own and execute against a published roadmap. Predictable, visible execution is a core expectation of this role.
• Maintain, expand, and refine our AI work companion based on user feedback and emerging needs.
• Build integrations between Claude and our internal tools (e.g., Ashby, Wonderlic, Fireflies, Asana, Google Chat) using webhooks, MCP connectors, and APIs. Favor reusable patterns over one-off solutions.
• Run enablement: training sessions, documentation, AMAs, and 1:1 onboarding to help every TENEX'er become more AI-native.
• Contribute to AI governance, monitoring, and security tooling decisions as TENEX scales its AI usage.

AI Enablement Engineer

• Partner with stakeholders to understand business needs, operational pain points, and workflow inefficiencies across Marketing and Communications
• Analyze current-state workflows to identify opportunities for AI-enabled process improvement, automation, and redesign
• Serve as a consultant to teams on how to evolve ways of working through AI, agents, and automation
• Facilitate discussions that clarify problems, desired outcomes, and opportunities for workflow transformation
• Translate business needs into clear use cases, workflow requirements, acceptance criteria, and success metrics
• Assess solution feasibility, expected value, scalability, effort, and risk
• Prioritize opportunities in alignment with team needs, business impact, and the broader AI strategy
• Help manage a pipeline of solution opportunities from intake through recommendation and delivery
• Design, build, test, and deploy AI-enabled solutions including agents, automations, workflow improvements, and supporting tools
• Own implementation for assigned solutions, coordinating requirements, testing, stakeholder input, and rollout
• Iterate on solutions based on performance, feedback, and evolving business needs
• Maintain and enhance deployed tools and workflows to improve effectiveness and adoption over time
• Ensure solutions align with enterprise policies, governance requirements, and responsible AI practices
• Support teams in adopting redesigned workflows and integrating AI-enabled solutions into day-to-day operations
• Monitor usage, outcomes, and performance to identify gaps and opportunities for refinement
• Document reusable patterns, use cases, and recommendations that can be scaled across the function

Business Communications Advisor – AI Workflow and Solutions

• Set the multi-year strategy for ML, data, and BI
• Build and scale the Data Science / AI, Data Engineering, and BI Engineering organization
• Manage managers and tech leads
• Run performance management, career development, and retention
• Operate as a peer to VP-level leaders across various functions
• Own the architecture, deployment, and operation of the production ML

VP of AI, ML and Data Strategy

• Lead a team responsible for identifying, designing, and executing AI and automation solutions
• Lead a team of approximately 10 automation professionals
• Ensure high-quality delivery and effective support for internal stakeholders
• Oversee identification and definition of AI and automation solutions aligned to business strategy
• Review and guide design recommendations
• Provide hands-on design direction for complex initiatives
• Partner with delivery, engineering, and architecture teams
• Serve as a senior liaison between business leaders and technical teams
• Guide evaluation of emerging AI and automation technologies

Operations Manager – AI & Automation Design

• Own the HBS AI Champion Pilot Program and the Microsoft 365 Copilot Cowork Program rollout end-to-end.
• Deliver role-based enablement and career-development upskilling for Microsoft 365 Copilot, Microsoft Copilot Cowork, and Claude agents through training sessions, certifications, prompt clinics, peer coaching, and ongoing reinforcement.
• Recruit and support each Champion cohort in partnership with department leaders: produce the Champion Commitment Guide and Manager Fact Sheet, coach champions through 1:1 sessions and group office hours, and run structured peer-coaching sessions with their teammates.
• Drive cross-department engagement through use-case challenges and visible recognition.
• Track adoption analytics (daily active users, license utilization, prompt library usage, certification completions) and publish a monthly one-page dashboard against documented baselines.
• Capture candid pilot feedback from Cowork participants and synthesize it into actionable adoption, governance, and product insights for the Director of AI and the Executive Leadership.
• Curate and version-control role-based prompt libraries, custom Cowork skills, and agent templates; maintain a searchable internal knowledge base of resolved issues, prompt patterns, and best practices; embed Microsoft 365 Copilot learning paths and Copilot Cowork modules into the HBS internal training site.
• Act as the strategic bridge between business stakeholders and the technical AI delivery team by contributing business context, prompt design input, and adoption-readiness review on custom solution work, and pilot new Microsoft and Claude AI capabilities internally before they reach client engagements.
• Operate the AI Helpdesk tier-1 and tier-2 queue, contribute to the refinement of internal AI delivery methods and certification curricula, and apply the AI Use Policy in daily work while flagging exceptions to the Director of AI.

AI Enablement Specialist

• Define and own the AI product strategy, outcomes, and end‑state vision, aligning WIM AI initiatives to enterprise priorities and measurable business value.
• Lead end‑to‑end product management for complex, enterprise‑grade AI platforms, solutions, and capabilities, ensuring scalability, governance, and operational readiness.
• Partner closely with the WIM Head of AI & Digital Asset Platforms to shape AI roadmaps, investment decisions, prioritization, and product tradeoffs.
• Translate business problems into prioritized AI use cases, value cases, and delivery plans across advisor, client, and operations domains.
• Drive cross‑product AI portfolio management, identifying dependencies, synergies, and risks across platforms, workflows, and distribution channels.
• Guide long‑term AI solution delivery, accounting for regulatory requirements, responsible AI principles, data dependencies, and enterprise technology constraints.
• Articulate AI product vision and roadmap; define scope; prioritize backlogs; and support platform evolution, model deployment, and workflow integration.
• Own key business, adoption, operational, and risk metrics, embedding governance, controls, and compliance requirements throughout the AI product lifecycle.
• Engage and align cross‑functional teams and external partners, including Product Owners, Engineering, Data, Risk, Compliance, Legal, Operations, and vendors.
• Serve as a trusted advisor to senior leadership, translating complex AI, regulatory, and market considerations into clear product direction and execution plans.

Executive Director – Artificial Intelligence, AI

• Build, test, and deploy automation, monitoring, and analysis solutions for agent workflows
• Support deployment and scaling of automated agent-based solutions across development and production environments
• Investigate technical, operational, and reliability issues during development and implementation
• Build and integrate software tools that improve system reliability, observability, and scalability
• Support setup of development and production environments, including deployment-related activities
• Develop best practices and methodologies for provisioning, configuration management, monitoring, and troubleshooting
• Collaborate with delivery teams to define and track service level indicators, service level objectives, SLAs, and error budgets
• Create and recommend process improvements that increase stability, efficiency, and operational readiness
• Contribute to the ongoing improvement of operational processes for automated agent workflows

Senior Artificial Intelligence Operations Specialist

• Ensure that all transactions are processed accurately, timely, and completed in their entirety
• Participate in projects/project teams and client conversions
• Test new systems and programs to improve efficiency and effectiveness
• Perform comprehensive review of all investor documents to ensure accuracy
• Input investor information into database ensuring 100% accuracy
• Review the work of others specialists and provide constructive feedback
• Assist with writing and updating procedures

Operations Specialist I – AI

• Develop production-grade integration and automation solutions: Design and implement production-ready automation workflows that integrate cloud services, internal data sources, and LLM endpoints.
• Lead the development and deployment of domain-specific LLMs: Fine-tune lightweight models on internal data from Gong, Notion, BigQuery, and other sources, then make them accessible to teams across the organisation through intuitive interfaces.
• Establish best practices and patterns for AI and automation across the company: Create templates, documentation, and training materials that enable non-technical teams to build and deploy their own automation workflows confidently.
• Enable widespread adoption: Run hands-on workshops, provide ongoing support, and mentor teammates so anyone at UpGuard can leverage AI and automation tools effectively.
• Embed security & governance: Bake privacy, compliance, and cost controls into every workflow and model deployment, ensuring enterprise-grade reliability and data protection.

AI and Automation Engineer

• Support forecasting and staffing models by updating inputs, maintaining files, and validating trends
• Build and maintain dashboards and recurring reports for WFM and staffing metrics
• Help automate manual workflows across WFM (reporting, trackers, alerts, data pulls)
• Support recruiting and training planning by optimizing the hiring funnel, onboarding throughput, and ramp performance
• Assist with intraday and weekly workforce performance monitoring (volume vs staffing, SLA risk, schedule gaps)
• Document processes and create repeatable reporting/automation playbooks
• Interprets data, analyzes results, and provides insights to support data-driven decision-making for ad hoc and on-going reports
• Stay up-to-date with industry technologies and frameworks to identify trends to maintain best and cutting edge practices in AI
• Participate in special projects and initiatives as needed

Workforce AI Optimization Analyst

• Lead the evaluation and production readiness of AI products and capabilities across the enterprise.
• Ensure solutions are scalable, secure, governable, and aligned with enterprise architecture standards.
• Partner closely with Legal and Information Protection to meet regulatory and risk management requirements.
• Conduct GenAI intake reviews, applying enterprise AI guardrails and standards.
• Partner with solution teams and area technology leaders to clarify requirements and reduce redundancy.
• Steward enterprise AI architecture principles and requirements during path-to-production reviews.
• Advise cross-functional teams on solution architecture and data protection optimization.
• Serve as a strategic advisor on enterprise AI capabilities and opportunities.

Senior Architecture Advisor – AI Enablement

• Coordinate definition of USCC AI vision, target states, roadmap, and operating model
• Develop executive-level presentations and storytelling for the USCC OM&AI portfolio
• Devise and execute a communication plan with central Citi, central OM&AI, and USCC comms teams
• Cultivate an AI-forward culture across all levels of USCC via ideation forums, feedback loops, and engagement
• Partner with Citi Learning to develop and scale AI education and training programs; foster AI fluency across business lines
• Work with USCC AI Leads to ensure effective information sharing and adoption, including coordinated efforts to train users, leaders, and teams on AI tools
• Develop role-based and domain-specific learning paths
• Lead initiatives and workstreams end‑to‑end, including problem definition, root cause analysis, solution development, communication of outcomes, and securing buy‑in for change
• Manage all aspects of stakeholder engagement and “client service,” including planning, prioritizing, and organizing deliverables across multiple concurrent efforts
• Develop and maintain strong relationships with senior leaders and executives across USCC and partner organizations
• Identify opportunities to add value beyond formal scope through proactive insights, enhancements, and continuous improvement
• Efficiently address complex and ambiguous situations, exercising sound judgment and structured problem‑solving
• Balance multiple priorities effectively while meeting timelines and quality expectations
• Act as a subject‑matter expert and trusted advisor for OM&AI communications and change practices
• Operate with limited direct supervision; exercise independence of judgment and autonomy in execution decisions
• Contribute to team effectiveness and development through mentoring, coaching, and participation in recruiting or capability‑building efforts as appropriate
• Appropriately assess risk when making business decisions, with consideration for Citi’s reputation and control environment
• Ensure adherence to applicable laws, rules, regulations, and internal policies, applying sound ethical judgment and escalating issues transparently

Communications and Change Management Lead

• Develop and maintain core School of Splunk Field Sales Enablement portfolio
• Design and deliver AI-focused onboarding and upskilling curricula
• Integrate critical technical sales skills into curriculum for learners to develop mastery
• Compose test questions, evaluations, and assessments that validate proficiency
• Develop and continuously refresh learning content that reflects the messaging and positioning related to Splunk platform
• Work with key stakeholders to develop technical seller training programs
• Serve as a trusted advisor to technical sales stakeholders and SMEs
• Update and optimize existing GFE training assets to reflect Cisco's current strategic direction

Curriculum Designer – AI Native

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Search entry-level jobs
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Search senior-level jobs
Search jobs by tech stack
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Remote jobs Anywhere in the World
Companies Hiring Anywhere in the World
Companies Hiring Sales People Anywhere in the World
Companies Hiring Software Engineers Anywhere in the World
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Interview questions and answers
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Popular Jobs
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Director – AI Ethics and Governance

• Partner with stakeholders to understand workflows, challenges, and inefficiencies
• Identify and prioritize AI/automation opportunities based on impact and feasibility
• Translate business needs into clear use cases and requirements
• Design, build, and deploy AI agents and automation tools
• Support use cases such as spend visibility, supplier insights, risk monitoring, RFQs, contract analysis, and workflow automation
• Develop both prototypes and production-ready solutions using modern AI tools and APIs
• Rapidly prototype, test, and refine solutions with end users
• Deploy tools into live workflows and drive adoption through training and support
• Leverage internal data to power AI-driven insights
• Integrate solutions with ERP and procurement systems as needed
• Identify new opportunities for AI within procurement

Manager, AI Solutions – Automation

• Coordinate definition of USCC AI vision, target states, roadmap, and operating model
• Develop executive-level presentations and storytelling for the USCC OM&AI portfolio
• Devise and execute a communication plan with central Citi, central OM&AI, and USCC comms teams
• Cultivate an AI-forward culture across all levels of USCC via ideation forums, feedback loops, and engagement
• Partner with Citi Learning to develop and scale AI education and training programs; foster AI fluency across business lines
• Work with USCC AI Leads to ensure effective information sharing and adoption, including coordinated efforts to train users, leaders, and teams on AI tools
• Develop role-based and domain-specific learning paths
• Lead initiatives and workstreams end‑to‑end, including problem definition, root cause analysis, solution development, communication of outcomes, and securing buy‑in for change
• Manage all aspects of stakeholder engagement and “client service,” including planning, prioritizing, and organizing deliverables across multiple concurrent efforts
• Develop and maintain strong relationships with senior leaders and executives across USCC and partner organizations
• Identify opportunities to add value beyond formal scope through proactive insights, enhancements, and continuous improvement
• Efficiently address complex and ambiguous situations, exercising sound judgment and structured problem‑solving
• Balance multiple priorities effectively while meeting timelines and quality expectations
• Act as a subject‑matter expert and trusted advisor for OM&AI communications and change practices
• Operate with limited direct supervision; exercise independence of judgment and autonomy in execution decisions
• Contribute to team effectiveness and development through mentoring, coaching, and participation in recruiting or capability‑building efforts as appropriate
• Appropriately assess risk when making business decisions, with consideration for Citi’s reputation and control environment
• Ensure adherence to applicable laws, rules, regulations, and internal policies, applying sound ethical judgment and escalating issues transparently.

OM&AI Communications and Change Management Lead

Director, AI Product Definition – Execution

• Serve as EnerSys’ executive leader and single point of accountability for enterprise AI strategy, execution, and outcomes.
• Translate enterprise and business strategies into a clear, multi‑year AI vision and roadmap aligned with growth, productivity, quality, safety, and risk objectives.
• Advise the CIO, ELT, and Board on AI trends, risks, opportunities, and competitive implications.
• Represent EnerSys externally with strategic AI partners, technology providers, and industry forums.
• Design, implement, and continuously evolve the enterprise AI operating model, including governance forums, funding and intake processes, prioritization methods, and decision rights.
• Chair and run the Executive AI Steering Committee, ensuring disciplined decision making, clear accountability, and execution follow through; set agendas, align key stakeholders, and communicate decisions and actions clearly.
• Establish enterprise standards for responsible AI, data usage, security, privacy, and risk management in partnership with Legal, Cybersecurity, IT Architecture, HR, and Compliance.
• Own AI investment governance, ensuring alignment with capital planning, transformation funding, and enterprise portfolio priorities.
• Own the global AI use case portfolio and prioritization framework, balancing short term value delivery with long term strategic capability building.
• Ensure AI initiatives progress efficiently from ideation to pilot to scaled production—eliminating “pilot only” stagnation.
• Define, track, and report enterprise AI value metrics, including operational efficiency, cost reduction, quality improvement, risk mitigation, revenue enablement, and customer impact.
• Regularly recalibrate the AI roadmap based on realized outcomes, business performance, and evolving priorities.
• Orchestrate cross-functional delivery across IT, data, digital platforms, business units, and external partners—creating shared plans, clear ownership, and fast decisions.
• Resolve enterprise level dependencies and escalations impacting delivery, adoption, or value realization by facilitating trade-off discussions, aligning decision makers, and driving timely, documented outcomes.
• Ensure AI initiatives are integrated with core enterprise platforms (ERP, data environment, digital products) rather than delivered as isolated solutions.
• Build scalable execution playbooks, delivery standards, and reusable components to accelerate adoption across regions and functions.
• Partner with HR and Communications to lead enterprise-wide change management, AI literacy, and workforce enablement.
• Sponsor AI capability building, including training programs, citizen developer models, communities of practice, and leadership education.
• Drive cultural adoption by positioning AI as a core business capability embedded in how work gets done—not as a technology experiment.
• Support workforce transformation planning, including role evolution, ethical considerations, and future skills development.
• Build, lead, and develop a high performing enterprise AI organization with clear roles, succession, and career paths.
• Set performance expectations, accountability, and development plans for direct and indirect reports.
• Foster a strong culture of accountability, collaboration, and execution discipline across the AI ecosystem.

VP Enterprise AI Enablement

• Design and implement agentic AI architectures, including multi-agent workflows, tool-calling systems, memory and state management, and orchestration logic for complex multi-step tasks.
• Develop and evaluate retrieval-augmented generation (RAG) pipelines, with attention to retrieval strategy, document chunking and indexing, embedding model selection, re-ranking, and end-to-end evaluation.
• Contribute to LLM evaluation and validation frameworks — defining test coverage, constructing evaluation datasets, assessing output reliability, and identifying failure modes through structured testing and adversarial analysis.
• Conduct prompt engineering and instruction design for domain-specific tasks, and support experimentation with parameter-efficient fine-tuning approaches where applicable.
• Perform model benchmarking and comparative analysis, including evaluation of commercial and open-source LLMs for specific task types, latency and cost tradeoffs, and domain adaptation requirements.
• Support integration of LLM and agentic components with broader system architectures, including data pipelines, APIs, and domain-specific tooling.
• Contribute to data preparation and preprocessing workflows for structured and unstructured industrial datasets, including cleaning, transformation, and schema design.
• Engage in the full engineering rigor expected of production AI/ML systems — including unit and integration testing, model verification and validation, experiment evaluation, simulation workflows, data and output visualization, and technical documentation and reporting — as continuous activities throughout the project lifecycle.

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