Presale Solution Architect (AI)

Rubius; United States; Remote; USD 120,000 - 180,000; Full time

Job summary

Rubius is seeking a middle-level Presale Solution Architect to engage with clients and develop AI-driven solutions. You will translate business needs into technical architectures and demonstrate the capabilities of generative AI and intelligent systems.

You will participate in presales activities, preparing technical proposals and presentations, and collaborate with internal teams to ensure a smooth transition of developed concepts to the development team.

Qualifications

• Mid-level presale architect with a strong AI/ML background.
• Ability to translate business needs into scalable AI architectures.
• Experience presenting technical solutions to diverse audiences.

Responsibilities

• Engage with clients to identify needs and translate them into AI-driven architectures.
• Develop prototypes and proof-of-concept solutions showcasing capabilities of generative AI.
• Lead presales activities, preparing technical proposals and presentations.
• Collaborate with internal teams to ensure smooth handoff to development.

Skills

AI

Machine Learning

Cloud platforms

Job description

Rubius seeks a middle-level Presale Solution Architect to engage with clients and develop AI-driven solutions. Ideal for candidates with a strong background in AI, ML, and cloud platforms who can translate business needs into technical architectures.

In this role, you will work closely with clients to identify their needs and translate them into effective technological solutions using AI tools. Your responsibilities will include developing prototypes and proof-of-concept solutions while demonstrating the capabilities of generative AI and intelligent systems.

You will also participate in presale activities, preparing technical proposals and presentations to showcase AI solutions. As a technical expert, you will collaborate with internal teams to ensure a smooth transition of developed concepts to the development team, contributing to the overall success of AI implementations.