AI Solutions Architect — Tech Lead

About the role

You'll join a small, senior AI Tiger Team embedded directly into mid-market portfolio companies — assessing current-state tech landscapes, designing AI-enabled workflows, and standing up production-grade solutions before rotating to the next company. This is a hands-on delivery role across manufacturing, healthcare, SaaS, financial services, and more — no long discovery phases, no slide-deck consulting.

What you'll do

• Design end-to-end AI/ML architectures — from data ingestion through inference, output, and feedback loops
• Rapidly assess ERP/CRM landscapes (SAP, Salesforce, NetSuite) and wire in AI workflows cleanly
• Build automation layers using Zapier, N8N, Make, or equivalent low-code/no-code tooling
• Deliver working prototypes architected for reuse across future portfolio deployments
• Interface directly with CTOs, CIOs, and senior leadership — communicate progress, risk, and decisions without a translator
• Apply AI-assisted SDLC tooling (Cursor, Copilot, Claude) as standard practice across the full delivery lifecycle

What you bring

• 10+ years overall; minimum 3 years delivering hands-on AI solutions in enterprise environments
• Demonstrated track record taking AI initiatives from POC to production at scale
• Hands-on Claude / Anthropic platform expertise — specific requirement, not general LLM familiarity
• Production-grade experience on Azure AI Services (primary); AWS Bedrock or Google Vertex AI a strong plus
• API integration, webhook architecture, and event-driven workflow design experience
• Proficiency in prompting as an engineering discipline — chain-of-thought, few-shot, structured output, version-controlled prompt design
• Prior multi-client, fast-paced, or PE-backed environment experience is a strong differentiator

Nice to have

• Opinionated, current view of the AI landscape — foundation models, agentic systems, orchestration frameworks — tied to what's production-ready today
• Experience designing human-in-the-loop workflows with confidence thresholds, override mechanisms, and audit trails
• Familiarity with multi-agent and compound AI orchestration patterns in enterprise contexts