AI Solutions Architect

Role Overview

• Lead technical discovery with customers to understand workflows, constraints, data realities, and success criteria—and translate that into a clear technical plan.
• Architect end-to-end AI systems (GenAI + traditional ML when needed), spanning data ingestion, retrieval, orchestration, evaluation, deployment, and UX.
• Design and deliver rapid prototypes that make abstract ideas concrete and de-risk key decisions (cost/latency/reliability, integration feasibility, quality thresholds).
• Develop reference architectures and technical frameworks that demonstrate what’s possible with current and emerging AI technologies.
• Guide clients through the AI landscape—capabilities, limitations, tradeoffs, and what it takes to operate AI systems in production over time.
• Partner with sales teams to translate technical concepts into clear value propositions, proposals, business cases, and technical presentations.
• Define technical requirements and scalable architecture patterns for production-grade systems (security, governance, observability, evaluation, and reliability).
• Collaborate with fulfillment to ensure solutions align with customer needs and leverage Tribe AI’s capabilities—and help create smooth handoffs from pre-sales to delivery.
• Stay current on frontier developments, bringing in SMEs from the Tribe network and collaborating with partners (including OpenAI/Anthropic) to keep customers ahead of the curve.
• Act as a trusted advisor to technical and executive stakeholders navigating AI implementation and adoption challenges.

Requirements

• 5+ years leading technical workstreams; experience as a Solutions Architect, or a similar customer-facing technical role in the AI, cloud, data platform, or backend infrastructure space.
• Strong customer-facing instincts: you can run discovery, ask the right questions, and turn ambiguity into clear architecture and next steps.
• Experience scoping projects and estimating effort to build end-to-end applications (including assumptions, risks, milestones, and dependencies).
• Strong fluency in Generative AI and the evolving LLM ecosystem and you’ve built or consulted on AI/GenAI systems in production.
• Expertise with production cloud infrastructure (AWS, Azure, and/or GCP) and modern deployment patterns.
• Systems thinker who can balance speed and rigor: you’re comfortable making tradeoffs and explaining them to both engineers and execs.
• Excellent communication skills (written + verbal): you can make complex technical concepts feel simple and actionable.

Tech Stack

• AWS
• Azure
• Cloud
• Google Cloud Platform

Benefits

• Competitive salary
• Equity package
• Health insurance
• Unlimited Vacation time