GenAI Solution Architect
Amazon Web Services•Published 23 hours ago•First seen 21 hours ago
Amazon Web Services (AWS) is leading the next phase of AI adoption with GenAI and Agentic AI technologies and is seeking a hands-on AI/ML engineer to design, build, and operate production AI systems.
This role sits at the intersection of deep AI understanding, industry context, and execution. You will work hands-on with customers ensure use cases move from intent to deployable, production-ready 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.
Key job responsibilities
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, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying.
Why AWS?
Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses.
Inclusive Team Culture
Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon conferences, inspire us to never stop embracing our uniqueness.
Mentorship & Career Growth
We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional.
Work/Life Balance
We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud.
This role sits at the intersection of deep AI understanding, industry context, and execution. You will work hands-on with customers ensure use cases move from intent to deployable, production-ready 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.
Key job responsibilities
- Architect & Operate Autonomous Agent Ecosystems: Design and deploy multi-agent systems where specialized agents collaborate to solve high-value business problems, progressing from R&D prototypes to enterprise-grade production environments.
- Lead Full-Stack AI Development: Own the end-to-end stack, including scalable backends for model serving (vLLM/SGLang/TGI), high-performance frontend interfaces for AI interaction, and robust API orchestration layers.
- Establish Agentic Governance & Security: Implement advanced security controls, including "Human-in-the-Loop" protocols to ensure responsible and compliant AI operations.
- Optimize System Performance: Evaluate and tune systems for managing the trade-offs between model quality, latency, throughput, and token efficiency.
- Advanced MLOps & AgentOps: Standardize CI/CD pipelines, automated testing for non-deterministic AI outputs, model versioning, and deep observability (tracing/drift detection).
- Integrate Legacy & Modern Systems: Bridge modern GenAI components with existing enterprise workflows, ensuring data consistency across vector stores, graph databases, and traditional RDBMS.
- Technical Leadership & Mentorship: Lead hands-on deep dives and technical workshops, contributing reusable code, reference architectures, and internal technical assets for the broader engineering organization.
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, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying.
Why AWS?
Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses.
Inclusive Team Culture
Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon conferences, inspire us to never stop embracing our uniqueness.
Mentorship & Career Growth
We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional.
Work/Life Balance
We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud.
Basic Qualifications
- Deep fluency in PyTorch or TensorFlow, Hugging Face, and modern LLM serving infrastructures, with demonstrated experience using advanced agentic frameworks such as Strands, LangGraph, or CrewAI to manage complex, stateful workflows
- Expert-level skills in Python and at least one additional language such as Java, Go, or TypeScript, underpinned by a strong foundation in modern backend architectures.
- Hands-on experience with AWS ecosystems (including Bedrock, AgentCore, and SageMaker) to set up secure, private-network AI environments, and practical experience implementing Retrieval-Augmented Generation using embeddings, vector stores, and semantic search optimization.
- Strong track record in implementing automated testing, observability (including tracing AI reasoning paths), and secure deployment strategies for AI/ML models
Preferred Qualifications
- Experience in running & fine-tuning Large and Small Language Models using advanced techniques like LoRA/QLoRA, Instruction Tuning, and RLHF to optimize for specific domain tasks.
- Demonstrated leadership in defining and implementing advanced LLMOps practices, including evaluation frameworks for non-deterministic outputs, centralized tracing for complex reasoning paths, and real-time drift detection.
- Expertise in architecting AI systems within highly regulated or security-sensitive environments (e.g., Financial Services, Healthcare, Public Sector).