Enterprise Solutions Architect (AI)

TL;DR

Enterprise Solutions Architect (AI): Driving the implementation of generative AI applications for enterprise customers with an accent on inference optimization, fine-tuning methodologies, and scalable model serving. Focus on leading technical sales cycles, architecting production-grade RAG and agentic workflows, and influencing the product roadmap for a high-performance LLM infrastructure platform.

Location: Must be based in New York, NY or San Mateo, CA

Company

A fast-growing, Series C generative AI infrastructure company building high-performance, scalable model serving platforms backed by leading investors like Sequoia and Benchmark.

What you will do

• Serve as the primary technical advisor for executive-level enterprise clients.
• Lead technical sales cycles from scoping to high-impact proof-of-concepts.
• Architect production-grade GenAI solutions including advanced RAG and AI agents.
• Synthesize customer feedback to directly influence the product roadmap.
• Create technical content such as reference architectures and developer workshops.
• Represent the company as a technical expert at conferences and industry events.

Requirements

• Bachelor's degree in Computer Science or equivalent practical experience.
• 7+ years of experience in customer-facing technical architecture roles.
• At least 3 years of experience in pre-sales or consulting capacities.
• Deep hands-on expertise with the LLM stack, inference, and fine-tuning.
• Strong Python development and debugging skills.
• Exceptional communication skills for articulating complex technical concepts.

Nice to have

• Master's or PhD in Computer Science or Machine Learning.
• Experience with cloud infrastructure (AWS, GCP, Azure), Docker, and Kubernetes.
• Proven track record of influencing strategy and closing multi-million dollar deals.

Culture & Benefits

• Opportunity to work at the forefront of AI infrastructure.
• Collaborative environment with engineers from Meta and Google.
• Fast-paced culture with high ownership and low bureaucracy.
• Direct impact on the global future of enterprise AI adoption.