Amazon Web Services (AWS) is leading the next phase of AI adoption. The ASEAN Technology Team is seeking a hands-on Senior Solution Architect, AI Engineering — a forward-deployed technical leader who converts AI ambition into production systems that customers can operate and scale.

This is not an advisory role. You embed directly with AWS customers across ASEAN to architect, build, and deploy AI and Agentic solutions in production environments. You combine deep domain expertise in Generative AI, large-scale system design, and customer-facing consulting to turn complex challenges into repeatable, scalable outcomes. You are the person who makes AI real — from first discovery conversation to production launch and beyond.

As part of the ASEAN Technology Team reporting to the ASEAN Head of Technology, you will operate as a trusted technical advisor across the region's most strategic AI engagements. You'll partner with account teams, specialist SAs, ProServe, and the AWS Partner Network to accelerate customer adoption of GenAI/ML and Agentic technologies — while feeding insights back to AWS service teams to influence roadmap.

Key job responsibilities
Customer Delivery & Forward Deployment

• Embed with customers to lead end-to-end AI solution delivery: discovery, technical scoping, architecture design, hands-on build, production deployment, and operational handover.
• Replace manually intensive or proof-of-concept AI workflows with scalable, production-grade systems tailored to customer environments.
• Architect secure, cost-optimized AI solutions leveraging AWS services (Bedrock, AgentCore, SageMaker, and broader AWS ecosystem) that meet enterprise requirements for reliability, governance, and performance.
• Drive measurable outcomes: solutions deployed, workloads in production, engineering hours saved, and business value realized.

Technical Leadership & Thought Leadership

• Serve as the AI domain expert across ASEAN, providing guidance on architecture patterns, emerging technologies, and best practices for GenAI/ML and Agentic workloads.
• Lead technical deep-dives, workshops, and executive-level design sessions with customer engineering, business, and leadership teams.
• Create and share reusable reference architectures, code samples, technical content (whitepapers, blogs, workshops), and operational playbooks for customers, partners, and the AWS Technical Field.
• Influence customer and internal business decision-makers as a technical thought leader, translating complex AI concepts into tangible business outcomes.

Enablement & Community

• Coach and enable customer engineering teams on practical AI adoption — from prompt engineering to production MLOps and Agentic orchestration.
• Contribute patterns, learnings, and outcomes to the broader AWS specialist community, accelerating collective capability across the region.
• Act as the voice of the customer internally — advocating for roadmap enhancements, capturing feedback, and anticipating requirements by working backwards from customer needs.

A day in the life

You work alongside customer engineering teams during live AI implementation sprints — debugging inference pipelines, optimizing RAG architectures, tuning agent orchestration, or designing evaluation frameworks. You spend your time identifying what's blocking production readiness, prototyping solutions, and driving adoption of what works.

Between engagements, you're producing reference architectures, leading workshops, or briefing account teams on AI strategy for upcoming customer conversations. You move fast, build working systems, learn from deployments, and share what works across the region.

About the team

The ASEAN Technology Team is a group of specialist technologists embedded across Southeast Asia, focused on accelerating customer adoption of AWS through deep technical expertise and hands-on engagement. The team covers AI/ML, Data, Application Modernization, and Infrastructure — partnering closely with sales, ProServe, and specialist organizations to drive the most complex and transformative customer workloads in the region.

Basic qualifications

• 7+ years of IT development or implementation/consulting in the software or Internet industries experience
• 5+ years of design, implementation, or consulting in applications and infrastructures experience
• Experience in developing and deploying LLMs in production on GPUs, Neuron, TPU or other AI acceleration hardware, or experience debugging, profiling, and implementing best software engineering practices in large-scale systems
• Experience communicating across technical and non-technical audiences and at C-level, including training, workshops, publications

Preferred qualifications

• Master's degree or above in computer science, mathematics, statistics, machine learning or equivalent quantitative field, or PhD
• AWS certification, such as, AWS Solutions Architect, or a similar cloud certification, or Associate's degree or above
• - Expertise in architecting AI systems within highly regulated or security-sensitive environments (e.g., Financial Services, Healthcare, Public Sector, Telecommunications).