Solutions Architect, Formant

• 2–4 years in a customer-facing technical role (Solutions Engineering, Implementation Engineering, Technical Support Engineering, or similar)
• Exposure to LLMs, prompt engineering, or AI/ML workflows (production experience a plus but not required) and appetite working with AI tools
• Experience deploying technical solutions into production environments
• Strong programming skills in Python or TypeScript
• Comfortable working in ambiguity and learning new domains quickly
• Willingness to travel for customer deployments (25–35%)
• Who Will Thrive in This Role:
• Builders who prefer shipping working solutions over debating theoretical ones
• Engineers who enjoy interacting with customers and seeing their work deployed in the real world
• Curious generalists who can go from debugging logs to discussing workflow improvements
• Team players who collaborate closely across engineering, product, and GTM

Desirable

• We’re hiring a Solutions Architect/Forward Deployed Engineer to be an external customer champion and help bring AI capabilities into real-world physical environments
• This role is ideal for someone who has experience with and enjoys working directly with external customers, solving messy operational challenges, and shipping practical AI-powered solutions. You won’t be working in isolation on research problems — you’ll be partnering with customers and internal teams to implement solutions that create measurable value
• You’ll work closely with senior GTM leadership and product engineering to deploy, refine, and scale AI-driven workflows across our customer base. Over time, you’ll grow into deeper ownership of deployments and strategic accounts
• Support and implement end-to-end AI deployments for customers, from discovery through production rollout
• Participate in on-site customer engagements (approximately 25–35% travel)
• Work with external stakeholders to translate customer operational needs into technical requirements and working solutions
• Troubleshoot and resolve production issues in collaboration with engineering
• Help document best practices and implementation playbooks
• Build and iterate on LLM-powered workflows, RAG pipelines, and AI-assisted automation tools
• Prototype quickly while balancing reliability and performance
• Integrate with customer systems and APIs
• Navigate real-world physical constraints (latency, connectivity, hardware limitations)
• Contribute reusable components that accelerate future deployments
• Support existing external customers and resolve their potential issues
• Partner with product engineering to relay field feedback and improve platform capabilities
• Support technical conversations during customer engagements
• Help enable customer teams through documentation and training materials
• Contribute insights that shape product roadmap and deployment strategy