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.

Data & Machine Learning Foundations

• Strong working knowledge of data management concepts, including:

• • Data quality, lineage, governance, and lifecycle considerations

• • Feature engineering and data readiness for ML

• Collaborate closely with internal and client data platform teams (this role does not own data platforms but must design against them).

• Apply a solid foundation in statistics and applied machine learning to ensure:

• • Models are architected appropriately

• • Assumptions, limitations, and risks are well understood and communicated

Executive Communication & Consulting Leadership

• Lead business‑level and AI‑level conversations with C‑suite and senior leadership.

• Translate complex technical architectures into clear business narratives tied to value, risk, and outcomes.

• Provide trusted advisory guidance on AI strategy, operating models, and investment decisions.

• Contribute to the development of repeatable consulting offers, assessments, and delivery frameworks.

• 10+ years Consulting experience leading client discovery, workshops, and executive readouts.

• Proven experience as an AI Architect, AI Solution Architect, or equivalent role.

• Prior hands‑on experience as an AI Engineer or ML Engineer building real AI solutions of moderate to advanced complexity.

• Strong architecture background across cloud, security, integration, and scalability.

• Excellent written and spoken English.

• Experience designing or operating AI Centers of Excellence.

• Multicloud experience across Azure, AWS, and GCP.

• Business management or business operations experience enabling strong understanding of client needs and constraints.

• Spanish business professional fluency.