AI infra GTM Specialist, Service Specialist Orgnization

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Would you like to be the person who ensures the AI infrastructure ambitions of CMHK customers translate into real workloads running on AWS? As a Go-to-Market Specialist (GTMS) for AI Infrastructure, you are the connective tissue between CMHK customers, field account teams, and global AWS service teams. You own the go-to-market motion for AWS's AI infrastructure portfolio — EC2 GPU instances, SageMaker AI, and Trainium — and your job is to make adoption happen: bring the voice of the customer to service teams, equip field teams to win, and keep everything oriented toward workloads actually landing and running.

You operate at the intersection of customers and builders. Half your time is spent with customers and account teams — understanding their AI training and inference roadmaps, shaping commercial structures, and unblocking the path to deployment. The other half is spent working across global WWSO and service teams — carrying CMHK market requirements into product and capacity decisions, driving feature prioritization, and ensuring the global machine moves for your customers. You are neither pure field nor pure product; you are the person who holds the full picture and makes sure no part of the chain drops.

You do not just sell a service — you see the entire value chain from capacity planning through commercial negotiation to workload go-live, and you orchestrate the right people at the right time to make each stage succeed. You combine market awareness of the GPU and accelerator ecosystem with the commercial instincts to structure deals and the operational rigor to drive them to closure. Success is measured in workloads landed, revenue closed, and customers scaled — not in meetings held or decks produced.

The ideal candidate understands the AI infrastructure market at a structural level — supply dynamics, chip economics, competitive positioning — and can translate that understanding into actionable GTM plans. You should be comfortable discussing instance selection and capacity models with a customer's ML platform team, then turning around to build a business case that moves a service-team leader to prioritize a regional requirement. You are self-driven, commercially sharp, and relentless about outcomes.

What Makes This Role Different
Beyond the standard GTMS mandate, we are hiring against four differentiating capabilities. Candidates will be assessed explicitly against each.

1. Market and ecosystem fluency in AI infrastructure
You understand the AI infrastructure landscape — not just AWS's portfolio, but the broader GPU and accelerator chip ecosystem, supply-demand dynamics, competitive alternatives, and how customers make build-vs-buy and chip-selection decisions. You track market shifts (new silicon generations, capacity constraints, pricing movements) and translate them into GTM implications. This is not about being a chip engineer; it is about having the commercial and strategic literacy to advise customers and position AWS credibly in a fast-moving market.

2. Full-stack GTM ownership with a workload-landing mindset
You do not consider a deal closed when a contract is signed — you think in terms of workloads running in production. You hold the mental model of the entire customer journey: from initial engagement through commercial negotiation, capacity securing, onboarding, and production scale-up. While you do not personally execute every step, you ensure nothing falls between the cracks. You coordinate SA, CSM, and service-team resources like a program leader, and you escalate or intervene when progress stalls. Your north star is adoption, not booking.

3. Commercial acumen and deal-shaping ability
You bring genuine business negotiation skills. You understand how to structure AI infrastructure engagements — commitment models, pricing levers, capacity reservations — and you work with account teams to shape deals that are compelling for the customer and sound for AWS. You are not a bystander in commercial discussions; you actively craft the offer, quantify the value, and help close.

4. Influence across boundaries — customer, field, and service teams
This role lives at the seams of the organization. You must earn trust and drive action in three directions simultaneously: with customers who need to believe AWS can deliver at their scale, with field teams who need your expertise to win, and with global service teams who need to be convinced that a CMHK requirement deserves priority. You build relationships fast, communicate with clarity and conviction, and push things to resolution rather than letting them queue. When something is stuck, you diagnose whether the blocker is technical, commercial, or political — and you drive the specific action to clear it.

Key job responsibilities
Customer Engagement & Demand Shaping

Engage directly with CMHK customers' AI/ML platform teams and decision-makers to understand their training and inference roadmaps, infrastructure preferences, and adoption timelines; build trusted relationships that give you early, accurate signal on where demand is heading.

Translate customer requirements into structured asks — capacity needs, feature gaps, commercial constraints — and carry them with context and conviction to global service teams and WWSO, ensuring the customer's voice lands with specificity rather than abstraction.

Go-to-Market Execution & Deal Progression

Own the end-to-end GTM motion for the AWS AI infrastructure portfolio in CMHK — define the positioning, build the pipeline, and drive opportunities from qualification through closure in partnership with field account teams.

Shape deal structures with commercial acumen: advise on commitment models, capacity reservations, pricing levers, and engagement terms that balance customer value with AWS economics; be a hands-on participant in commercial negotiations, not a sideline observer.

Maintain a workload-landing orientation throughout the deal lifecycle — ensure that every closed opportunity has a credible path to production deployment, and orchestrate SA, CSM, and service-team resources to remove blockers before they stall adoption.

Market Intelligence & Ecosystem Awareness

Maintain current, defensible knowledge of the AI infrastructure market — GPU supply dynamics, accelerator chip roadmaps (NVIDIA, AWS custom silicon, competitive alternatives), pricing trends, and how customers evaluate build-vs-buy decisions across the ecosystem.

Translate market intelligence into actionable GTM guidance for account teams: which customers to prioritize, which workloads to target, where AWS has a right-to-win versus where competitive positioning requires a different approach.

Prove a repeatable GTM motion on a real customer win, codify it, and transfer it to the broader field so that one successful landing pattern becomes a scalable playbook for AI infrastructure adoption.

About the team
Diverse Experiences
AWS values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying.

Why AWS?
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Inclusive Team Culture
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Work/Life Balance
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Basic Qualifications

- 3+ years of Go-To-Market, Business Development, Sales, or Consulting experience
- 3+ years of working with Data & AI related technologies, including, but not limited to, AI/ML, GenAI, Analytics, Database, and/or Storage experience
- Bachelor's degree or equivalent
- Experience in written and verbal communication skills to communicate with technical and non-technical audiences, including senior leadership

Preferred Qualifications

- Experience with cloud, server or infrastructure technologies and business models
- Experience working across a global, matrixed organization and influencing HQ service and product teams from an in-region or bridging role.
- Familiarity with the CMHK / Greater China market and its customer segments, or comparable experience representing a region's needs to a global product organization.
- Familiarity with AI/ML and generative AI infrastructure requirements, and how infrastructure and managed-services decisions constrain or enable them.

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