Job Description

We are looking for an AI-Native Product Architect to design, validate, and scale modern software and AI-powered solutions. This role combines hands-on engineering, solution design, rapid product development, and customer collaboration to transform ideas into practical, scalable business outcomes. This opportunity is open to current Software Engineers who want to transition into Solutions Engineering, valuing strong engineering fundamentals, curiosity about business problems, clear communication, and the ability to connect customer needs with technical execution.

Roles & Responsibilities

1. Design end-to-end business and technology solutions that align with stakeholder needs, enterprise constraints, and strategic objectives.
2. Define solution architectures, technology stacks, integration patterns, cloud strategies, and implementation roadmaps for software and AI-enabled systems.
3. Develop rapid MVPs, prototypes, and proofs of concept to validate opportunities, reduce delivery risk, and accelerate learning and time-to-market.
4. Design and implement AI-powered solutions including agents, copilots, RAG architectures, workflow automation, and intelligent business applications.
5. Create reusable AI harnesses, accelerators, prompt frameworks, and engineering workflows that improve productivity, quality, and AI adoption.
6. Partner with engineering teams to ensure solutions are scalable, secure, maintainable, observable, and cost-efficient, while growing your technical leadership skills.
7. Contribute to architecture standards, governance practices, reusable assets, and reference architectures that improve delivery consistency across projects.
8. Research and evaluate emerging AI, cloud, data, and engineering technologies, and communicate practical recommendations based on business value and tradeoffs.

Required Qualifications

1. At least 1-year of experience in strong software engineering fundamentals and sound judgment when reviewing, testing, and validating software, including AI-generated work.
2. Problem framing, systems thinking, and product judgment—the ability to understand customer needs, identify dependencies and tradeoffs, and connect solutions to user and business outcomes.
3. AI fluency and experimentation—the curiosity to use AI tools effectively, evaluate their output critically, and learn as the technology evolves.
4. Clear communication, collaboration, and comfort working through ambiguity with customers and engineering teams.