Principal Consultant, Artificial Intelligence (AI) (Remote - US)

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 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.

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.