AI Architect & Program Manager

Title: AI Architect & Program Manager

Location Minneapolis & Charlotte NC

Go-To-Market Lead (Digital Technology & Innovation)

Role Summary

This is a hybrid GTM Forward-Deployed Engineering role focused on driving the adoption of Generative AI and Agentic AI solutions across the enterprise. The ideal candidate must balance strategy stakeholder management technical product leadership and hands-on coding/prototyping.

Key Responsibilities

• Go-To-Market Leadership: Define and execute AI platform adoption strategies drive enterprise usage of LLMs agent frameworks APIs and orchestration platforms.
• Forward-Deployed Engineering: Build prototypes demos proof-of-concepts and production-ready AI solutions while working directly with business teams.
• AI Solution Architecture: Design RAG systems vector search multi-agent systems tool-calling workflows and memory frameworks.
• Developer Enablement: Conduct workshops and create SDKs prompt libraries templates reusable components and playbooks.
• Stakeholder Engagement: Present AI concepts and business outcomes to executives and leadership teams.
• Responsible AI & Governance: Ensure compliance with security risk regulatory and Responsible AI standards.

Required Qualifications

• 4 years of AI experience.
• 3 years in GTM leadership technical product management solution engineering or forward-deployed engineering.
• Hands-on GenAI/LLM development experience.
• Strong programming background.
• Experience with Azure or GCP and container technologies such as Docker and Kubernetes/OpenShift.

Preferred Skills

• Generative AI: LLMs Prompt Engineering Embeddings Vector Databases and RAG.
• Agentic AI: LangChain LangGraph AutoGen ADK CrewAI.
• LLMOps: Evaluation observability guardrails and prompt versioning.
• AI Development Tools: GitHub Copilot Cursor Devin and similar platforms.

Ideal Candidate Profile: Technical enough to build AI applications strategic enough to drive adoption customer-facing experienced in enterprise AI implementation and comfortable operating in regulated environments.

Suggested Screening Questions

1. Describe an AI/LLM solution you built end-to-end.
2. How have you implemented RAG architectures
3. Have you worked with LangGraph AutoGen or multi-agent systems
4. What cloud platform experience do you have (Azure/GCP)
5. How do you measure AI product adoption and business impact
6. Have you used GitHub Copilot Cursor or Devin
7. How do you address AI governance and compliance
8. Tell us about a prototype that became a production solution.

Overall Fit: Best suited for AI Solution Architects AI Product Managers with coding skills Forward-Deployed Engineers GenAI Consultants and AI Platform Adoption Leads with expertise in LLMs Agentic AI RAG Cloud Platforms and Enterprise Stakeholder Management.