GenAI Solutions Architect: Enterprise AI & GTM Strategy

Amazon Web Services (AWS); Austin (TX); On-site; USD 154,000 - 208,000; Full time

Job summary

Amazon Web Services (AWS) is seeking a Generative AI Solutions Architect to design and deliver GenAI solutions for enterprise customers. You will build GenAI-powered applications using Bedrock and SageMaker, fine-tune models, and guide customers through deployment and adoption strategies at scale.

You will work with product owners to shape GenAI offerings, collaborate with data scientists and solution architects, and create technical guidance, reference implementations, and field enablement

Qualifications

• 7+ years in IT or software programming, cloud computing, or data & AI domains with GenAI focus
• 5+ years in design/implementation/consulting for ML/AI solutions using ML frameworks
• Deep understanding of LLMs, training/inference lifecycles, and model optimization
• Proficiency in prompt engineering, function calling, and retrieval evaluation

Responsibilities

• Advise customers and deploy state-of-the-art GenAI solutions.
• Drive thought leadership via blogs, white papers, reference architectures, and events.
• Collaborate with Data Scientists, SAs, Sales, and GenAI service teams to accelerate adoption.
• Provide customer-driven product feedback to AWS GenAI services teams.
• Develop internal GenAI communities and field enablement materials.

Skills

GenAI architecture

LLM fundamentals

Prompt engineering

RESTful API design

Python/Java

ML frameworks

Education

Master's degree in quantitative field

Tools

LangChain

LLAMAIndex

Data Augmentation

Evaluation frameworks

Job description

Amazon Web Services (AWS) is seeking a Generative AI Solutions Architect to design and deliver GenAI solutions for enterprise customers. You will build GenAI-powered applications using Bedrock and SageMaker, fine-tune models, and guide customers through deployment and adoption strategies at scale.

You will work with product owners to shape GenAI offerings, collaborate with data scientists and solution architects, and create technical guidance, reference implementations, and field enablement