GenAI GTM Lead for Startups — North America

Amazon Web Services (AWS)

New York (NY)

On-site

USD 180,000 - 240,000

Full time

8 days ago

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Job summary

Amazon Web Services (AWS) is seeking a Sr. GTM Specialist for GenAI to define and lead the startup GTM strategy for Bedrock across North America.

You will partner with Sales, Solutions Architects, Partners, and Marketing to move startups from proof of concept to production, land lighthouse customers, and drive revenue. You will advise startups on model selection, retrieval augmented generation, and inference cost tradeoffs, and collaborate with model providers to align GTM motions and reference

Qualifications

  • 6+ years of Go-To-Market, Business Development, Sales, or Consulting experience.
  • 5+ years of technology domain experience (software, cloud, systems, data, analytics).
  • Experience designing and executing joint marketing plans with global partners.
  • Bachelor's degree or equivalent required.
  • 5+ years working with Data & AI technologies including GenAI, Analytics, databases, and storage.

Responsibilities

  • Own the startup GTM strategy for GenAI across North America, including market sizing, sales plays, and pipeline goals.
  • Engage founders and engineering leaders to move Bedrock/GenAI workloads from PoC to production and drive revenue.
  • Advise startups on model selection, retrieval augmented generation, agents, and cost tradeoffs.
  • Collaborate with GenAI providers to build joint GTM and co-selling motions for startups.

Skills

Go-To-Market
Business Development
Sales
Consulting
GenAI/Bedrock
Public Speaking

Education

Bachelor's degree or equivalent

Job description

Amazon Web Services (AWS) is seeking a Sr. GTM Specialist for GenAI to define and lead the startup GTM strategy for Bedrock across North America.

You will partner with Sales, Solutions Architects, Partners, and Marketing to move startups from proof of concept to production, land lighthouse customers, and drive revenue. You will advise startups on model selection, retrieval augmented generation, and inference cost tradeoffs, and collaborate with model providers to align GTM motions and reference

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