New opportunity

AI, Data & Cloud Director

RoShay Services · New York, United States

About this role

Lead AI, data, and cloud transformation initiatives using Google Cloud AI services. The director will advise C-level stakeholders and deliver multi-agent conversational AI systems integrated with enterprise workflows to improve customer experience and operational efficiency.

Responsibilities

  • Lead end-to-end AI, data, and cloud programs; advise clients on AI strategies and roadmaps.
  • Design, architect, and deliver scalable conversational AI and automation systems.
  • Identify market opportunities, develop proposals, and expand the AI portfolio through business development.
  • Manage and mentor cross-functional teams, and keep solutions aligned with AI advancements, Google Cloud innovations, and digital strategy.

Requirements

  • At least 8 years in data analytics, AI/ML, and cloud technology, with leadership experience; a bachelor’s degree is required, and an advanced degree is preferred.
  • Deep expertise in the Google Cloud AI ecosystem, including Agent Builder, Dialogflow, and Vertex AI, plus experience architecting AI-powered multi-agent and conversational systems.
  • Understanding of AI scaling, ethical AI, MLOps, and enterprise integration, with strong communication skills for engaging senior stakeholders.
  • Authorization to work in the U.S. without sponsorship.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

Google CloudAgent BuilderDialogflowVertex AIConversational AIMulti-agent systemsAI/MLData analyticsCloud technologyMLOpsEthical AIEnterprise integrationAI strategySolution architectureTeam leadershipBusiness developmentStakeholder communication

Your skill match

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YOUR NEXT STEP

Get interview-ready for this role

A focused preparation guide, built around this job’s responsibilities and requirements.

✦ AI-generated guide
Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.
01

Understand the role

What matters most

  • Lead transformation programs end to end

    The director leads AI, data, and cloud programs using Google Cloud AI tools, from strategy through delivery.

  • Advise senior stakeholders on AI direction

    The role advises clients on AI strategies and roadmaps and communicates with C-level stakeholders.

  • Deliver enterprise conversational AI

    A central responsibility is architecting scalable multi-agent conversational AI and automation systems integrated with enterprise workflows. The posting names Agent Builder, Dialogflow, and Vertex AI.

  • Grow the AI portfolio

    The director identifies market opportunities, develops proposals, and contributes to business development.

  • Lead teams while keeping solutions current

    The role manages and mentors cross-functional teams and aligns solutions with AI advancements, Google Cloud innovations, and digital strategy.

Likely challenges to think through

  • Scaling without weakening controls

    Likely preparation challenge, not a reported employer problem: multi-agent systems must scale and integrate with enterprise workflows while accounting for ethical AI and MLOps. Prepare an architecture example that explains reliability, oversight, and integration trade-offs.

  • Balancing executive ambition with delivery

    Likely preparation challenge: advising C-level stakeholders on roadmaps while leading delivery can require prioritizing achievable outcomes over an expansive vision. Prepare to explain how you would sequence work and connect technical decisions to customer experience or operational efficiency.

  • Balancing portfolio growth with team capacity

    Likely preparation challenge: proposals and new market opportunities can compete with delivery and mentoring time. Prepare a reasoned approach to assessing opportunities, setting scope, and coordinating cross-functional teams without assuming a particular company process.

03

Practice your answers

Technical practice

How would you choose among Agent Builder, Dialogflow, and Vertex AI for a conversational AI solution?

What to coverThe posting names all three as areas of expected expertise. Suggested preparation: explain how you would match their capabilities to a use case and justify trade-offs in control, complexity, and maintainability.

How would you architect a multi-agent conversational system?

What to coverThe role calls for designing and delivering multi-agent systems. Suggested preparation: describe how agents would divide responsibilities, share context, coordinate actions, and handle an unsuccessful handoff.

How would you design a conversation that can complete an enterprise workflow?

What to coverThe posting connects conversational AI with automation and enterprise workflows. Suggested preparation: address intent handling, confirmation before consequential actions, and what happens when the system cannot complete a request.

How would you integrate an AI solution with existing enterprise systems?

What to coverEnterprise integration is an explicit qualification. Suggested preparation: explain how you would establish system boundaries, manage access and data exchange, and respond when a dependent system is unavailable.

What would you consider when scaling a conversational AI service?

What to coverThe role calls for scalable systems and an understanding of AI scaling. Suggested preparation: discuss how demand, response time, cost, and reliability affect architecture choices, without assuming a particular traffic level.

How would you apply MLOps to an AI solution after launch?

What to coverMLOps is explicitly named in the posting. Suggested preparation: cover evaluation before release, monitoring in operation, and how you would investigate or reverse a change that reduces quality.

How would you address ethical AI risks in a customer-facing conversation?

What to coverThe posting asks for an understanding of ethical AI and aims to improve customer experience. Suggested preparation: discuss risks such as inappropriate outputs or exposure of sensitive information, plus proportionate testing, safeguards, and escalation.

How would you evaluate whether an AI initiative improved customer experience and efficiency?

What to coverThose outcomes are stated in the role overview, alongside data and analytics work. Suggested preparation: propose suitable measures, establish a baseline, and distinguish useful automation from interactions that merely appear successful.

How would you turn an AI strategy into an executable roadmap?

What to coverThe director is expected to advise clients on AI strategies and roadmaps and lead end-to-end programs. Suggested preparation: explain how you would prioritize opportunities, test feasibility, sequence delivery, and revisit decisions as evidence emerges.

How would you troubleshoot a conversational system that repeatedly fails to complete requests?

What to coverThe role includes delivering conversational AI integrated with enterprise workflows. Suggested preparation: separate conversation-design, agent-coordination, model, and integration failures before choosing a remedy and checking its effect.

Behavioural practice

Tell me about a time you advised senior stakeholders on an AI or cloud decision.

What to coverThe role involves advising C-level stakeholders. For this suggested practice question, use a real situation, explain how you communicated options and trade-offs, and state the decision or result.

Describe a time you led a cross-functional technology program from planning through delivery.

What to coverEnd-to-end program leadership and cross-functional team management are stated responsibilities. For this suggested practice question, describe the situation, your coordination and decisions, and the delivery result.

Tell me about a time you helped a team member or team improve its technical work.

What to coverThe posting calls for mentoring and fostering technical excellence. For this suggested practice question, give a real example of the need, the support you provided, and what changed.

Describe a time you developed a proposal for a new technology opportunity.

What to coverIdentifying market opportunities and developing proposals are stated responsibilities. For this suggested practice question, explain the opportunity, your contribution to the proposal, and its outcome without assuming it won.

Tell me about a time you reassessed a technology direction as capabilities changed.

What to coverThe role includes staying current with AI and Google Cloud innovations while aligning with digital strategy. For this suggested practice question, describe the situation, how you evaluated the change, and the resulting decision.

02

Sharpen your knowledge

Topics to prepare

  • Google Cloud AI service landscape

    Review the roles of Agent Builder, Dialogflow, and Vertex AI; make a comparison sheet explaining when you would consider each in a conversational AI design. The posting explicitly calls for deep expertise in all three.

  • Multi-agent conversational architecture

    Sketch how agents would coordinate, maintain conversational context, and handle failures in an enterprise workflow. This revises the posting’s explicit focus on scalable, multi-agent conversational AI systems.

  • Scaling, MLOps, and ethical AI

    For a hypothetical conversational system, list the decisions needed to scale it, monitor its performance, and address ethical risks. The posting explicitly requires understanding of AI scaling, MLOps, and ethical AI.

  • Enterprise integration and data flow

    Trace how a customer request could move through a conversational system and an enterprise workflow, noting data handoffs and integration boundaries. This supports the stated goal of improving customer experience and operational efficiency through integrated solutions.

  • AI strategy and portfolio development

    Outline how to prioritize a small set of AI opportunities by business value and delivery feasibility, then turn the leading option into roadmap milestones. The role includes advising clients on strategy, identifying market opportunities, and expanding the AI portfolio.

Skills to revise

  • Present a solution architecture

    Practice a short whiteboard walkthrough of a multi-agent conversational solution using the named Google Cloud AI services, with clear integration points and scaling trade-offs. The director is expected to architect and deliver such systems.

  • Plan an end-to-end program

    Draft a one-page delivery plan covering discovery, architecture, implementation, and rollout, with cross-functional responsibilities and decision points. This rehearses the stated responsibility for leading AI, data, and cloud programs.

  • Brief a C-level stakeholder

    Prepare a three-minute explanation of a proposed AI initiative, its expected customer or operational outcome, major risks, and decisions needed. The posting calls for advising C-level stakeholders and communicating with senior leaders.

  • Develop a proposal outline

    Write a concise proposal outline that connects a market opportunity to a client problem, proposed solution, and delivery approach. Developing proposals and business opportunities is an explicit responsibility.

  • Lead a design review

    Rehearse facilitating a cross-functional review: state the architecture decision, solicit objections, resolve a trade-off, and assign follow-ups. This is a preparation exercise for the posting’s team leadership, mentoring, and technical-delivery responsibilities, not a specified interview format.

04

Your preparation checklist

  • Map the stated qualifications

    Prepare a factual summary of relevant data analytics, AI/ML, cloud and leadership experience. Note the posting’s minimum eight years, bachelor’s degree requirement, advanced-degree preference and U.S. work-authorization requirement without assuming you meet them.

  • Build an end-to-end program walkthrough

    If you have a relevant example, prepare a concise account of the strategy, roadmap, delivery decisions, cross-functional leadership and measured results. The role calls for leading AI, data and cloud programs and advising clients.

  • Practice a conversational AI design exercise

    Sketch a hypothetical multi-agent system using appropriate roles for Agent Builder, Dialogflow and Vertex AI. Show enterprise workflow integration, scalability, failure handling and how you would assess customer-experience or efficiency outcomes; label the design as a preparation exercise.

  • Prepare an executive roadmap briefing

    Draft a short, hypothetical C-level briefing that prioritizes AI opportunities, explains trade-offs and proposes phased delivery. Include how ethical AI and MLOps would be addressed, since both appear in the qualifications.

  • Outline a proposal discussion

    Prepare a sample opportunity assessment and proposal outline covering client need, solution scope, value hypothesis and delivery risks. The posting explicitly includes market opportunities, proposals and AI portfolio growth.

  • Select a team-leadership example

    If applicable, prepare one example showing how you managed and mentored a cross-functional team, resolved a delivery trade-off and communicated the decision to senior stakeholders.

  • Write questions for RoShay Services

    Ask which client problems and enterprise workflows are priorities, how the director balances delivery with business development, and what outcomes would define success for the first programs. These are questions to clarify, not stated company plans.

AI-generated from the job information · Prepared Sep 30, 2026 (UTC)Useful preparation, not a guarantee of eligibility or an offer.

Availability last checked: Nov 4, 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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