AI GTM Lead

We need to output HTML fragment with sections: Use

for section headings: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. We must include sections only if posting has real specific content. Let's parse. Job Description: includes description of role, responsibilities. So we can have About the role (maybe summary) and Responsibilities. We have Required Qualifications and Desired Qualifications. Could combine into Requirements and Qualifications? The instruction: Use headings for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Skills). So we need to decide which sections to include. We have location info: Ability to work onsite in locations. That's not a heading; we can fold into a short

maybe under About the role. We have Pay: "starting pay range for this role is [88 - 90 $ W2 hourly range]." That's specific pay info, so we should include Pay section. Benefits: "This position is eligible for company benefits including participation in medical, dental, and vision insurance, flexible spending or health savings account, and AD&D insurance, employee assistance, participation in a 401k program, and additional voluntary or legally-required benefits". That's specific benefits, so include Benefits. Schedule: Not mentioned. No schedule info. So we should NOT include Schedule. Skills: Could be derived from desired qualifications (like communication skills, etc). But we have Qualifications sections. Perhaps we can have Requirements (required qualifications) and Qualifications (desired qualifications) or Skills. The instruction says we can use Skills heading if there is real specific content. We have desired qualifications that include many skill items. Could put under Skills. But we also have Required Qualifications. Might be better to have Requirements (required) and Qualifications (desired). Or combine into Requirements and Qualifications. Let's follow typical: Requirements = required qualifications; Qualifications = desired qualifications (or Skills). We'll include both. We also need About the role: a brief summary from job description. We'll craft a paragraph summarizing role. Responsibilities: list of duties from description. We must preserve all substantive information, not invent or summarize away detail. Must not omit details. So we need to include all details from description, responsibilities, qualifications, pay, benefits, location. We must not repeat job title as heading. Not include Job ID etc. We must remove boilerplate: EEO, accessibility, etc. Also remove "About NTT DATA" maybe? That's company description; could be considered boilerplate? It's not required but might be considered substantive? The instruction: Remove cookie/navigation/legal/EEO/equal-opportunity/scam-warning boilerplate, duplicated lines, and application instructions. The "About NTT DATA" is not exactly boilerplate but could be considered company overview; not required to keep? It's not part of job description but could be considered extra. Safer to omit it as it's not directly about the role. The instruction says preserve ALL substantive information; does company overview count as substantive? Might be considered not essential to the job posting. Usually we keep it? But they said remove boilerplate; company overview might be considered boilerplate. I'd omit. Also remove the lines about contact us for accessibility, EEO policy, etc. Now craft HTML. We need to use

for each section heading. Inside sections: use

for prose,

• for lists. We need to include location info: maybe a short paragraph under About the role: "Ability to work onsite in one of the following locations: San Francisco Bay Area, CA; Charlotte, NC; Dallas, TX; Phoenix, AZ; New York City, NY." Now responsibilities: need to extract list items from description. Let's parse responsibilities from the job description text: - Serve as the critical bridge connecting product, engineering, end users, and line-of-business stakeholders. - Shape the go-to-market strategy for AI platform capabilities, driving the adoption of machine learning and predictive AI solutions across both on-premises and cloud environments. - Deliver clear guidance, scalable enablement, and strong cross-functional partnerships to support emerging capabilities and maximize value. - Lead platform releases and feature rollouts across business and technology domains, coordinating closely with product and engineering teams to manage technical dependencies, risks, and escalations. - Orchestrate onboarding, training, documentation, and support programs that enable teams to seamlessly integrate AI into their development workflows. - Maintain a deep understanding of customer workflows, gather insights that inform roadmap priorities, enhance usability, and establish scalable platform patterns. - Ensure readiness and compliance across governance, security, and responsible AI frameworks. - Deliver executive-ready communications that highlight adoption trends, business impact, and quality metrics—championing a feedback-driven, customer-centric go-to-market approach that accelerates time to value and scales AI success across the organization. - Act as an advisor to senior leadership in developing a large cross functional team to identify and execute complex artificial intelligence initiatives that span a large line of business. - Provide consultation to more experienced leaders in order to recommend solutions which solve business challenges. - Lead the strategy and resolution of highly complex challenges ensuring the solution delivers the intended benefits. - Leverage Artificial Intelligence expertise to evaluate technological readiness, data availability, and resources required to execute the proposed solutions. - Provide direction on key issues which may arise during development or implementation. - Cultivate relationships with clients to identify and develop a pipeline of future Artificial Intelligence opportunities which align with the strategic priorities of the business. - Collaborate and consult with peers, colleagues, and managers to resolve issues and achieve goals. That's a lot. We'll put them as
  • . Now Required Qualifications: list: - 7+ years of Artificial Intelligence Solutions experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education - 3+ years experience across product/solution management, program delivery, or technical product ownership for AI/ML platforms, or cloud-native solutions - 3+ years hands-on experience with cloud technologies (GP, or Azure) and container orchestration (Docker, Kubernetes/OpenShift) Note: "GP" maybe typo for GCP? We'll keep as given. Desired Qualifications: list many items. We'll put under Qualifications or Skills. Let's decide to put under Qualifications (desired). We'll list each bullet. Desired qualifications bullets: - 5+ years of experience across the AI/ML lifecycle, including data management, feature engineering, model development, deployment, monitoring/observability, and model governance and risk management - Proven experience operating within large enterprise environments (regulated industry experience preferred) and building scalable, production-grade platforms - Hands-on expertise with Generative AI and agentic AI frameworks, including LLMs, diffusion models, RAG, and tool-based agents; familiarity with ecosystems such as Azure OpenAI, Hugging Face, LangChain/LangGraph, ADk, and vector databases - Strong experience with MLOps/LLMOps practices and tooling, including model registries, CI/CD pipelines, feature stores, prompt and chain versioning, evaluation frameworks, guardrails, and monitoring - Exceptional communication skills with the ability to translate complex technical concepts into clear, actionable insights and influence senior stakeholders - Working knowledge of modern AI/ML platform components and services, such as Vertex AI, BigQuery, OpenShift, IBM Cloud Pak for Data (CP4D), and distributed model execution platforms - Demonstrated ability to develop and execute executive-level roadmaps and dashboards that clearly articulate business impact, adoption trends, and risk posture - Deep technical acumen with the ability to engage at a detailed level across APIs, SDKs, security controls, data contracts, and system performance - Strong negotiation, stakeholder management, and cross-functional leadership capabilities, with a track record of driving alignment and outcomes across diverse teams Now Benefits: list: - participation in medical, dental, and vision insurance - flexible spending or health savings account - AD&D insurance - employee assistance - participation in a 401k program - additional voluntary or legally-required benefits Pay: The starting pay range for this role is [88 - 90 $ W2 hourly range]. Actual compensation will depend on a number of factors, including the candidate's relevant experience, technical skills, and other qualifications. We'll put that in a

    . Now About the role: we need a prose summary. Could be: "Seeking an AI Go to Market Lead to support Corporate Strategy, Digital Platforms & Innovation, working to advance enterprise AI capabilities and accelerate the integration of cutting-edge innovation into customer-facing products and internal platforms. You will drive the adoption and delivery of enterprise AI/ML capabilities across the organization." plus location. We'll include location sentence. Now ensure we don't include any boilerplate like "About NTT DATA", EEO, etc. Now produce HTML fragment. We must not include or . Just the fragment. Let's construct:

    About the role

    Seeking an AI Go to Market Lead to support Corporate Strategy, Digital Platforms & Innovation, working to advance enterprise AI capabilities and accelerate the integration of cutting-edge innovation into customer-facing products and internal platforms. You will drive the adoption and delivery of enterprise AI/ML capabilities across the organization. Ability to work onsite in one of the following locations: San Francisco Bay Area, CA; Charlotte, NC; Dallas, TX; Phoenix, AZ; New York City, NY.

    Responsibilities

    • Serve as the critical bridge connecting product, engineering, end users, and line-of-business stakeholders.
    • Shape the go-to-market strategy for AI platform capabilities, driving the adoption of machine learning and predictive AI solutions across both on-premises and cloud environments.
    • Deliver clear guidance, scalable enablement, and strong cross-functional partnerships to support emerging capabilities and maximize value.
    • Lead platform releases and feature rollouts across business and technology domains, coordinating closely with product and engineering teams to manage technical dependencies, risks, and escalations.
    • Orchestrate onboarding, training, documentation, and support programs that enable teams to seamlessly integrate AI into their development workflows.
    • Maintain a deep understanding of customer workflows, gather insights that inform roadmap priorities, enhance usability, and establish scalable platform patterns.
    • Ensure readiness and compliance across governance, security, and responsible AI frameworks.
    • Deliver executive-ready communications that highlight adoption trends, business impact, and quality metrics—championing a feedback-driven, customer-centric go-to-market approach that accelerates time to value and scales AI success across the organization.
    • Act as an advisor to senior leadership in developing a large cross functional team to identify and execute complex artificial intelligence initiatives that span a large line of business.
    • Provide consultation to more experienced leaders in order to recommend solutions which solve business challenges.
    • Lead the strategy and resolution of highly complex challenges ensuring the solution delivers the intended benefits.
    • Leverage Artificial Intelligence expertise to evaluate technological readiness, data availability, and resources required to execute the proposed solutions.
    • Provide direction on key issues which may arise during development or implementation.
    • Cultivate relationships with clients to identify and develop a pipeline of future Artificial Intelligence opportunities which align with the strategic priorities of the business.
    • Collaborate and consult with peers, colleagues, and managers to resolve issues and achieve goals.

    Requirements

    • 7+ years of Artificial Intelligence Solutions experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education
    • 3+ years experience across product/solution management, program delivery, or technical product ownership for AI/ML platforms, or cloud-native solutions
    • 3+ years hands-on experience with cloud technologies (GP, or Azure) and container orchestration (Docker, Kubernetes/OpenShift)

    Qualifications

    • 5+ years of experience across the AI/ML lifecycle, including data management, feature engineering,