Lead Artificial Intelligence Architect - Hybrid (Dallas or Houston, TX)

We are seeking a Lead AI Architect to shape the enterprise architecture for GenAI, ML, and agentic AI systems that power transformation across our global consulting business. In this high-impact role, you’ll define technical patterns, evaluate platforms, and guide the development of scalable, ethical, and value-driven AI capabilities. This position is ideal for a candidate with strong collaboration/communication skills as well as deep architecture expertise and practical experience building modern AI solutions in enterprise settings.  Candidate must be willing to work in an Agile delivery model and can help lead by doing hands-on coding for patterns, architectures, and proof of concepts. Why Join Us? This is a high-visibility opportunity to shape how our company embeds AI across its global operations. You’ll work at the intersection of architecture, innovation, and enterprise value creation, helping to define both near-term solutions and long-term strategy. This position will offer flexibility for hybrid work schedules to include both in-office presence and telecommute/virtual work to be based in Dallas or Houston, TX.Key Responsibilities: AI Architectural and Strategy:  Define and maintain the overarching GenAI, LLMs, RAG pipelines, and autonomous agent systems Evaluate tools, frameworks, and platforms (e.g., LangChain, Semantic Kernel, Vertex AI, Azure OpenAI, Bedrock) Identify and evaluate emerging Gen AI/AI technologies and trends, recommending their adoption where appropriate Design architectures that support composability, modularity, observability, and scalability of AI solutions, ensuring alignment with business strategy, data strategy, and technology roadmap Align architectural decisions with business performance, latency, security, cost, and explainability requirements Participate in the development of our AI Roadmap Solution Design:  Collaborate with data architects, data scientists, enterprise architecture, security, AI product managers, and business stakeholders Guide proof-of-concept development and prototype evaluations to test architecture decisions Provide architecture review, feedback, and mentorship to AI engineering teams Support build-vs-buy evaluations and provide input to platform roadmap decisions Governance: Embed responsible AI, privacy, and governance principles into system design Ensure AI architecture aligns with enterprise standards, security policies, and regulatory frameworks Partner with AI governance, data governance, security, and compliance teams to embed transparency and auditability