Artificial Intelligence Consultant (New York City Metropolitan Area)

AI Delivery Transformation Consultant – AIDLC

We are seeking experienced AI Delivery Transformation Consultants to help assess, validate, and certify teams operating within a new AI-driven delivery model. This role will focus on determining whether teams are truly adopting AI-enabled delivery practices in their day-to-day work, rather than relying solely on self-reported adoption.

The ideal candidate brings a strong technical or architecture background, hands-on experience with AI-enabled software delivery, and the ability to evaluate how teams are actually using AI tools, agents, skills, plugins, and automated workflows.

This is not a traditional Agile Coach role . We are looking for hands-on technical professionals who understand modern AI-driven SDLC practices and can objectively assess whether teams are changing how they build, deliver, and operate software.

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.