Principal Consultant, Artificial Intelligence (AI)

Lumen Dynamics empowers businesses to redefine what's possible with modern technology and human expertise. Our exceptional experience across Applications, Data & AI, DevOps, Security, and the Microsoft Azure platform enables organizations to accelerate innovation, enhance security, and optimize operational agility. As a Microsoft Partner with seven specializations, GitHub AI Partner of the Year, a member of the GitHub Advisory Board, and a member of the prestigious Microsoft Intelligent Security Association (MISA), Lumen Dynamics expertly delivers cutting-edge, integrated solutions that deliver business value. We are seeking a Principal Consultant to join our Data & AI practice and lead engagements from client discovery and value identification through use case portfolio development, ROI prioritization, and end‑to‑end solution architecture. This role also owns the design and establishment of AI Centers of Excellence (CoEs) for clients, ensuring AI adoption is governed, scalable, and aligned to business outcomes. This is a client‑facing, consultative role that sits at the intersection of executive strategy, applied AI engineering, and enterprise architecture. The Principal Consultant - AI must be equally comfortable whiteboarding with engineers, stress‑testing ROI with business leaders, and advising C‑suite executives on AI operating models and risk. Key Responsibilities Client Discovery & AI Readiness Assessment Lead structured discovery sessions (in-person and virtual) with executive stakeholders and technical SMEs to assess: Current‑state AI, data, cloud, and automation architecture. Business processes, decision points, and operational pain areas. Organizational readiness, governance maturity, and risk posture for AI adoption. Translate ambiguous client inputs into clear, actionable findings that inform both business and technical decisions. Produce discovery outputs that support executive alignment and downstream architecture decisions. Use Case Portfolio, ROI Stress Testing & Prioritization Identify, define, and document AI use cases across business functions, including: Business value hypothesis and success metrics. Technical feasibility, data dependencies, and delivery complexity. Build a use case portfolio and put each use case through an ROI stress test, prioritizing: Measurable business impact Feasibility and risk Time‑to‑value and scalability Create and present a priority matrix (impact × complexity × risk) and a sequenced AI adoption roadmap for executive decision‑making. AI Architecture & Solution Design Own the end‑to‑end architecture and design of complex AI, machine learning, and intelligent automation solutions, including: Generative and agentic AI architectures Predictive and supervised ML solutions Workflow automation and orchestration Secure integration with enterprise systems and data sources Define reference architectures, design patterns, and guardrails that ensure solutions are secure, scalable, governable, and production‑ready. Collaborate closely with AI Engineers, Platform Engineers, Data, Security, and Delivery teams to ensure architectural intent translates into successful implementation. AI Center of Excellence (CoE) Design & Enablement Design and help establish AI Centers of Excellence for clients, including: AI intake and qualification models Architecture and development standards Governance, Responsible AI, and risk controls Operating models for scaling AI across the organization Help clients move from ad‑hoc AI experimentation to repeatable, enterprise‑grade AI delivery. Enable client teams with frameworks, artifacts, and guidance that allow the CoE to operate independently over time. Platform & Ecosystem Expertise Deep familiarity with the Microsoft AI ecosystem, including: Microsoft Foundry, other Azure AI services including Azure Machine Learning Microsoft Fabric and related analytics patterns Copilot Studio and modern agent‑based AI approaches Comfortable architecting solutions on or translating architectures across: AWS Google Cloud Platform (GCP) While Microsoft Azure is the primary stack, the role requires credible multicloud fluency. Client Discovery & AI Readiness Assessment Lead structured discovery sessions (in-person and virtual) with executive stakeholders and technical SMEs to assess: Current‑state AI, data, cloud, and automation architecture. Business processes, decision points, and operational pain areas. Organizational readiness, governance maturity, and risk posture for AI adoption. Translate ambiguous client inputs into clear, actionable findings that inform both business and technical decisions. Produce discovery outputs that support executive alignment and downstream architecture decisions. Use Case Portfolio, ROI Stress Testing & Prioritization Identify, define, and document AI use cases across business functions, including: Business value hypothesis and success metrics. Technical feasibility, data dependencies, and delivery complexity. Build a use case portfolio a