Senior AI Consultant (Enterprise Assessment & Governance)

Location: Atlanta, Houston, Minneapolis (Hybrid)

Employment Type: Contract

About TURNBRIDGE

TURNBRIDGE Technical Solutions delivers precision-driven technical consulting and talent services for enterprise environments. We focus on quality, speed, and measurable outcomes—providing expert practitioners who accelerate transformation initiatives while reducing risk and complexity.

Role Overview

TURNBRIDGE is seeking a Senior AI Consultant to support an enterprise-wide AI transformation initiative. This engagement centers on evaluating and strengthening a large-scale AI portfolio, combining deep hands-on technical assessment with consultative governance and stakeholder enablement.

This is not a narrow build role—it's an enterprise transformation position requiring strong technical depth, structured thinking, and executive presence. You will collaborate across multiple business units and report into the broader AI program leadership structure.

What You’ll Do

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

Evaluate end-to-end ML workflows across multiple AI programs and create 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, registries, CI/CD for ML, feature stores, A/B testing frameworks)
• Governance and auditability (model cards, lineage, reproducibility standards)
• Organizational maturity using established frameworks (Google MLOps levels, ML Test Score) and develop a custom enterprise rubric

Deliverables include:

Clear maturity scoring, risk identification, and actionable recommendations.

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

Conduct technical assessments on a prioritized set of high-value production models.

You must be able to evaluate:

• Algorithm and architecture choice (classical ML, deep learning, transformers, ensemble methods)
• Fine-tuning and transfer learning approaches, including LLM/GenAI where applicable
• Training methodology (data splits, regularization, hyperparameters, compute efficiency)
• Feature engineering rigor and pipeline integrity
• Performance metrics tied to real business impact (e.g., precision/recall tradeoffs linked to operational KPIs)

This work requires real applied ML experience—not theoretical familiarity.

3. Consultative Facilitation & Governance Support

Serve as a technical credibility layer during AI scorecarding and governance discussions.

You will:

• Translate technical insights into business‑aligned narratives (e.g., precision → ticket reduction → OPEX impact)
• Support scorecard taxonomy development, helping teams define measurable KPIs & lineage
• Participate in workshops with AI program leaders
• Present findings to senior technical and executive-adjacent stakeholders
• Build concise, executive‑ready presentation materials summarizing assessments

(Note: You inform governance but are not the owner of scorecard deliverables.)

Ideal Background

• Several years of hands-on applied ML / data science experience
• Experience evaluating or auditing ML programs (platform teams, consulting, enterprise architecture, or governance roles)
• Comfort operating in ambiguous, transformation-driven environments
• Ability to engage senior technical leadership clearly and credibly
• Experience within large multi‑business‑unit organizations (telecom or similar complexity preferred)
• Strong executive communication skills and presentation capabilities

What This Role Is Not

• ❌ Not a full-stack engineering or production build role
• ❌ Not exclusively GenAI/LLM-focused — classical ML expertise is equally important
• ❌ Not the primary owner of scorecard deliverables

Why This Role Is Unique

This engagement offers a rare opportunity to shape AI maturity at enterprise scale. You will operate at the intersection of technical depth, governance architecture, and executive advisory—directly influencing how AI is measured, governed, and improved across a complex organization.