Senior AI Consultant (Enterprise AI Assessment & Governance)

Location: Atlanta, Houston, or Minneapolis (Hybrid)

Level: Senior to Principal

Engagement Type: Full-Time or C2C

Allocation: 100% Dedicated | ~60% Technical Assessment / ~40% Stakeholder Facilitation

Role Overview

We are seeking a Senior AI Consultant to join an active enterprise transformation engagement focused on evaluating and strengthening a large-scale AI portfolio.

This role operates in two primary modes:

Deep Technical Assessment – evaluating enterprise AI/ML systems, MLOps maturity, and production model quality.

Consultative Facilitation – serving as a technical bridge between AI program teams and executive stakeholders, informing AI scorecards, governance frameworks, and portfolio strategy.

This is not a narrowly scoped build role. It is a transformation-focused engagement requiring strong technical depth, structured thinking, and executive presence.

You will report into the AI Swimlane Lead and collaborate across multiple business units within a complex enterprise environment.

What You’ll Do

1. MLOps & Model Lifecycle Assessment (Enterprise-Level / Macro)

Evaluate end-to-end ML workflows across multiple AI programs and produce a maturity grading rubric tailored to a large enterprise.

You will assess:

• ML lifecycle processes: data ingestion, feature engineering, training, validation, deployment, monitoring
• MLOps tooling and patterns: experiment tracking, model registries, CI/CD for ML, feature stores, A/B testing infrastructure
• Governance and auditability: model cards, lineage tracking, reproducibility standards
• Organizational maturity frameworks (e.g., Google MLOps levels, ML Test Score) and adapt/build a custom rubric appropriate for a Fortune 20 environment
• Deliverable impact includes clear maturity scoring, risk identification, and practical recommendations for improvement.

2. Model-Level Technical Review (Deep-Dive / Micro)

Perform technical assessments on a shortlist of high-value production models.

You must be able to critically evaluate:

• Algorithm and architecture selection (classical ML, deep learning, transformer-based, ensemble methods)
• Fine-tuning and transfer learning approaches (including LLM/GenAI use cases where applicable)
• Training methodology: data splits, regularization, hyperparameter tuning, compute efficiency
• Feature engineering rigor and data pipeline integrity
• Model performance metrics in business context (e.g., precision/recall tradeoffs aligned to operational impact, not just accuracy scores)
• This requires hands-on applied ML experience and the ability to move beyond theoretical evaluation into practical enterprise constraints.

3. Consultative Facilitation & Governance Support

Act as a technical credibility layer within AI scorecard and governance discussions.

You will:

• Translate technical model performance into business-relevant language (e.g., model precision → call center ticket reduction → OPEX impact)
• Support scorecard taxonomy development by helping technical teams articulate measurable KPIs and data lineage
• Participate in stakeholder workshops with AI program leaders
• Present findings clearly to senior technical leaders and executive-adjacent audiences
• Build concise, executive-ready presentation materials summarizing assessment outcomes
• This role does not own scorecard deliverables, but materially informs them.

Ideal Background

• Several years of applied ML / data science experience
• Experience evaluating or auditing ML programs (internal platform team, ML consulting, enterprise architecture, or AI governance role)
• Comfortable operating in ambiguous, transformation-focused environments
• Strong communicator who can engage senior technical leaders without oversimplifying or hiding behind jargon
• Experience in large, multi-business-unit enterprises (telecom or similarly complex industries preferred but not required)
• Comfortable building and presenting executive-level decks summarizing technical findings

What This Role Is Not

• Not a full-stack engineering or production build role
• Not exclusively a GenAI/LLM specialist position — classical ML depth is equally valuable
• Not the primary owner of scorecard deliverables

Why This Role Is Unique

This is a rare opportunity to evaluate and influence AI maturity at scale within a large enterprise. You will operate at the intersection of technical depth, governance design, and executive advisory — shaping how AI is measured, governed, and improved across the organization.