Field-Facing AI Product Manager for Enterprise Outcomes

NextBigWhat; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

Amazon Web Services is seeking a product manager embedded in Forward Deployed Engineering, working inside enterprise customers to shape discovery, define outcomes, and deliver production AI/ML solutions. You will own engagements from discovery through delivery, while partnering with engineering to ship under compressed timelines.

This field-facing role requires travel ~30–50% and strong ability to translate customer learnings into platform improvements, with a focus on ROI and measurable

Qualifications

• Bachelor's degree in computer science, engineering, analytics, mathematics, statistics, IT or equivalent.
• 5+ years in product management, with experience in enterprise environments.
• Experience in building AI/ML products or working with agentic systems and enterprise workflow automation.
• Experience defining product requirements, writing PRDs, and setting success criteria.
• Experience engaging with Director-level or senior technical stakeholders.

Responsibilities

• Perform customer discovery and scoping with sales and senior PMs.
• Own customer outcomes inside enterprise environments with regulated data and legacy infrastructure.
• Follow AI DLC to define problems, write requirements, and deliver production code in weeks.
• Drive adoption by capturing feedback and proving business value.
• Contribute to GTM through customer stories and pricing/positioning input.

Skills

Product management

Enterprise engagement

AI/ML product experience

Stakeholder communication

Field-facing delivery

Education

Bachelor's degree in CS/Engineering/Analytics

Master's degree in Engineering/CS/OR/Business Analytics

Job description

Amazon Web Services is seeking a product manager embedded in Forward Deployed Engineering, working inside enterprise customers to shape discovery, define outcomes, and deliver production AI/ML solutions. You will own engagements from discovery through delivery, while partnering with engineering to ship under compressed timelines.

This field-facing role requires travel ~30–50% and strong ability to translate customer learnings into platform improvements, with a focus on ROI and measurable