GenAI Sales Manager

Amazon Web Services (AWS) is leading the next phase of AI adoption with GenAI and Agentic AI technologies and is seeking a senior technically strong leader who can translate AI capability into real customer outcomes.

This role sits at the intersection of deep AI understanding industry context and execution. You will work hands-on with customers to qualify AI use cases shape AI solutions and ensure initiatives move from intent to deployable production-ready systems. Success in this role depends on technical credibility delivery discipline and the ability to guide customers through real architectural and operating trade-offs in AI systems.

AWS Global Sales enables customers of all sizes to innovate and scale on the AWS cloud. As organizations across ASEAN move from experimentation to production with foundation models LLMs and agentic systems this role focuses on converting AI ambition into programs that can be delivered operated and scaled in production environments.

This is a quota-carrying role with revenue targets used as a measure of delivery impact. Quota credit is aligned to AI opportunities that reach production-ready scope reinforcing accountability for feasibility cost and execution quality rather than intent or pilots.


Key job responsibilities
- Originate qualify and own GenAI and Agentic AI opportunities end-to-end to production with account teams regional sales industry teams and specialists; Own/exceed GenAI/Agentic AI revenue targets; maintain accurate qualification and forecasting; build annual/territory plans to maximise revenue growth
- Translate customer business objectives into scalable secure and operable AI architectures across data pipelines foundation models RAG agentic patterns deployment and operations
- Act as technical authority by advising customers on AI maturity operating models responsible AI cost drivers risk model selection fine tuning and production trade-offs; build differentiate value for AWS.
- Lead executive workshops technical discovery and complex multi-stakeholder AI engagements balancing value feasibility cost and delivery risk
- Define relevant AI POVs with explicit success criteria timelines cost envelopes and clear production paths to differentiate AWS; collaborate with AI Solution architects and engineering teams to execute drive engagement progress towards closure
- Ensure AI solutions meet security regulatory reliability cost and operability requirements before production sign-off
- Govern partner and SI-led GenAI and Agentic AI implementations through architecture standards technical reviews and acceptance criteria
- Build internal AI field capability by creating technical content best-practice guidance and reusable reference architectures patterns and customer learnings and feed structured feedback into AI product roadmaps.
- Engage with AWS AI product teams to drive successful customer outcomes provide feedback influence roadmaps to ASEAN needs.


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

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Work/Life Balance
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- 7 years of experience in technology roles delivering or supporting AI ML or data-driven systems in customer-facing environments with a proven track record of technical sales and demonstrated work on complex multi-stakeholder AI sales engagements involving long sales and delivery cycles.
- Experience shaping influencing and closing AI- and data-led programs with the ability to translate AI technical concepts into clear business implications for both technical and executive audiences.
- Strong understanding of GenAI concepts and AI architectures including foundation models LLMs agent-based systems and RAG patterns as well as AI system design considerations such as AI data pipelines model lifecycle deployment and operations.
- Deep knowledge of AI cost drivers and AI unit economics including inference costs scale trade-offs and operating considerations supported by working knowledge of AWS AI/ML services such as Amazon Bedrock SageMaker or equivalent.
- Bachelors degree in Computer Science Engineering or a related field with a focus on AI ML or data systems.

- Hands-on experience building integrating or operating GenAI and ML systems in production and advanced POV environments with practical use of AI/ML frameworks such as PyTorch TensorFlow LangChain LlamaIndex or equivalent.
- Hands-on exposure to prompt engineering RAG implementation fine-tuning approaches and AI model evaluation techniques applied to real-world or near-production AI solutions.
- Experience deploying or governing AI systems in production or regulated environments with a background in solution architecture engineering or consulting roles involving AI system delivery.
- Familiarity with responsible AI practices governance frameworks and model risk considerations when designing deploying or overseeing AI solutions.
- Advanced degree (MS/PhD) in AI ML Computer Science or related fields providing strong theoretical and practical foundations for AI system development and delivery.

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