JOB 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 Google Cloud Platform 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/Google Cloud Platform)? 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.
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