Artificial Intelligence Consultant

Key Responsibilities: Review teams that have self-reported operating at defined AIDLC accelerator maturity levels. Validate whether teams are meaningfully incorporating AI skills, agents, plugins, and AI-driven workflows into their daily delivery practices. Assess team workflows, delivery processes, technical practices, and outcomes to determine actual adoption. Confirm that AI tools and plugins are properly registered within the enterprise AI catalog and aligned with the governed technology path. Evaluate whether team structures align with the intended AI accelerator model, including smaller, more efficient delivery pods versus traditional large teams. Use telemetry, delivery data, documentation, team discussions, and other evidence to validate adoption. Pressure test and refine existing AIDLC maturity levels and certification criteria. Help establish clear standards for what “good” looks like in AI-enabled software delivery. Identify gaps between self-reported adoption and actual implementation. Provide actionable feedback to teams and leadership regarding maturity, adoption, and areas for improvement. Feed insights and lessons learned back into enterprise AI guidance, governance, and operating model refinement. Partner with technical and business stakeholders across multiple teams and lines of business. Facilitate assessments in a constructive manner while maintaining an objective and evidence-based approach.

Required Qualifications: Hands-on experience delivering software or technology solutions using AI-enabled development practices. Strong technical, engineering, architecture, or software development background. Experience with AI-driven SDLC/AIDLC practices. Practical experience using AI coding tools, AI agents, skills, plugins, and workflow automation in day-to-day delivery. Ability to evaluate technical workflows, telemetry, delivery evidence, and outcomes. Experience assessing whether teams are genuinely changing how they build and deliver technology. Strong understanding of modern software engineering and delivery methodologies. Ability to operate effectively in ambiguous and evolving environments. Strong analytical and problem-solving skills with the ability to distinguish meaningful adoption from surface-level or self-reported adoption. Ability to quickly understand existing frameworks and contribute with minimal ramp-up time. Strong communication skills and the ability to work effectively with both technical teams and senior stakeholders.

Preferred Qualifications: Experience with AI governance, auditability, responsible AI, or enterprise AI controls. Familiarity with AI catalogs, plugin ecosystems, and governed AI technology environments. Experience developing or evaluating technology maturity models, certification frameworks, or assessment methodologies. Experience working across large enterprises, multiple business units, or complex technology organizations. Experience with AI-assisted software engineering and developer productivity platforms. Strong facilitation and stakeholder management skills. Ability to challenge teams and validate evidence without creating a punitive or audit-focused environment.