Description

Would you like to be part of a team shaping the future of the IT industry through responsible and innovative use of Artificial Intelligence? AWS seeks a Senior AI Specialist Sales professional to drive adoption of AWS's full stack AI services across Australia. Amazon Web Services (AWS) is advancing the next evolution of AI adoption. We are seeking a Senior AI Sales Specialist who brings strong technical AI knowledge together with a customer-centric, outcomes-focused approach to revenue growth. This role partners closely with customers to understand their goals, translate complex AI capabilities into clear business value, and support successful adoption at scale. If you enjoy collaborating with diverse stakeholders, communicating technical concepts with clarity, and contributing to long-term customer success, we encourage you to apply. Ability to engage with CEO/LOB owners for strategic conversations to leverage AI for business transformation, as well as with CTO/CAIO for AI-implementation linked technical architecture conversations. AI specialist solution architects will support technical deep dives. AWS Global Sales supports customers of all sizes across the region to innovate and grow in the cloud. As organizations increasingly adopt foundation models, large language models (LLMs), and autonomous AI agents, we are expanding our team of technical sales professionals who can identify opportunities, build sustainable pipelines, and guide customers through complex AI adoption journeys across the Asia Pacific Japan (APJ) region (or ASEAN / country-specific, as applicable).

Key job responsibilities

Sales & Revenue Contribution

• Support the achievement of sales targets by developing and maintaining a healthy pipeline and guiding customers through end-to-end sales cycles, from discovery through agreement
• Create thoughtful account plans and territory strategies in partnership with internal teams, while maintaining accurate forecasting using CRM tools
• Collaborate with customers to identify and qualify high-impact AI use cases aligned to their business priorities and strategic objectives

Technical Sales & Solution Advisory

• Act as a trusted advisor on AWS GenAI and Agentic AI services, helping customers understand potential outcomes, value, and return on investment
• Lead technical discovery sessions and contribute to demonstrations, proofs of concept, and solution design, including model selection, fine-tuning, and deployment approaches
• Provide guidance on model evaluation, responsible AI practices, governance considerations, and relevant compliance requirements

Customer Engagement & Market Development

• Build and maintain strong relationships with decision-makers and executive stakeholders through a consultative, collaborative approach
• Partner with internal teams to deliver workshops, enablement sessions, and solution reviews that support customer learning and adoption
• Share customer insights and feedback with AWS AI product teams to help inform product direction and improvements
• Engage C-suite executives to drive AWS AI services adoption: CEOs (business transformation), CTOs/CAIOs (technical architecture), CFOs (ROI models), line of business owners (use cases). Lead AI-focussed Executive Briefing Centers, board presentations, and strategic planning workshops.

Basic Qualifications

• Experience communicating technical concepts and processes using clear, simple language and visuals
• 7+ years of experience in technology roles, including technical or solution-oriented sales, with a demonstrated history of contributing to revenue goals
• Experience supporting complex, multi-stakeholder sales engagements and managing pipelines with a focus on accuracy and transparency

Preferred Qualifications

• Experience developing, deploying and managing AI products at scale
• Bachelor's degree in computer science, machine learning, engineering, or related fields, or Master's degree
• Experience working with Generative AI technologies, including foundation models, LLMs, or autonomous agent frameworks
• Familiarity with prompt engineering, retrieval-augmented generation (RAG) architectures, fine-tuning techniques, and AI deployment patterns