AI Engagement Leads

NTT DATA Services•Plano, TX

3d•$150,000 - $250,000•Hybrid

About The Position

As an AI Engagement Leader, you will be the primary accountable delivery leader for one or more complex client AI engagements. Reporting to an AI Delivery Executive, you will lead day-to-day client delivery, translate transformation objectives into executable plans, and orchestrate full-stack delivery pods that scale with the size and complexity of the engagement. You will own delivery outcomes, client confidence, engagement economics, scope, schedule, quality, risk, staffing, governance, and team performance. You will work across business, architecture, engineering, data science, project management, business analysis, DevOps/MLOps, quality, security, and support capabilities, including globally distributed teams and AI Studio Centers of Excellence.

Requirements

Bachelor’s degree or equivalent professional experience.
10+ years of experience in technology consulting, professional services, program delivery, product/digital delivery, or enterprise technology transformation, including significant leadership of complex client engagements.
10+ years total relevant experience, including approximately 5+ years leading complex client engagements or major workstreams.
Proven end-to-end ownership of multi-workstream technology programs or engagements, including responsibility for scope, schedule, budget, quality, risk, staffing, governance, and client outcomes.
Experience delivering AI, data, cloud, digital, application modernization, automation, or other complex technology transformation initiatives in enterprise environments.
Strong financial and commercial management capability, including revenue, margin, forecast, burn, resource planning, change control, and management of fixed-price, time-and-materials, outcome-based, or similar delivery constructs.
Demonstrated ability to build trusted relationships with senior client stakeholders and lead governance, expectation management, difficult conversations, and escalation resolution.
Experience leading multidisciplinary and geographically distributed teams across onshore, nearshore, offshore, and partner delivery models.
Strong understanding of delivery methodologies and controls, including Agile, hybrid, and/or program-management approaches, RAID management, quality management, and executive reporting.
Sufficient technical fluency to work effectively with architects, engineers, data scientists, platform teams, security teams, and business stakeholders and to understand technical trade-offs that affect delivery.
4+ years working closely with technical teams in complex delivery environments, with sufficient fluency to understand architecture, engineering, data, platform, and security trade-offs.

Responsibilities

Own end-to-end delivery of assigned AI engagements from mobilization and deal-to-delivery transition through implementation, production release, stabilization, and value realization.
Translate client business outcomes, contractual commitments, and technical scope into clear delivery plans, milestones, workstreams, ownership, dependencies, and measurable success criteria.
Serve as the primary day-to-day delivery leader for client stakeholders, building trusted relationships, maintaining transparent communication, and ensuring delivery remains aligned to business objectives.
Establish and run effective governance, including delivery reviews, RAID management, financial reviews, client status, steering committees, architecture and quality checkpoints, change control, and executive escalation as appropriate.
Own engagement financial health, including revenue, cost, margin, forecast, burn, staffing profile, utilization, planned versus actual effort, change requests, and commercial leakage.
Maintain strong scope and change discipline; identify unfunded or out-of-scope activity early and ensure the appropriate commercial conversation is initiated rather than allowing delivery teams to absorb uncontrolled work.
Lead multidisciplinary delivery pods that may include AI Architects, AI Engineers, Data Scientists, Data Engineers, Project Managers, Business Analysts, UI/UX, AI DevOps/MLOps, QA, support, security, FDEs, domain SMEs, and other specialist roles.
Scale delivery capacity to the engagement, managing resource onboarding, performance, upcoming roll-offs, skill gaps, and future demand in partnership with the AIDE, Resourcing, Technical Lead, and global capability teams.
Identify delivery risks and early-warning signals before they become escalations; establish mitigation, ownership, due dates, and escalation thresholds, and bring the AIDE clear facts, impact, options, and a recommended path when executive intervention is required
Partner with the Principal AI Domain Technical Leader for the vertical, architects, engineers, data scientists, security, platform, and client technical teams to resolve dependencies and technical delivery risks while retaining clear accountability for the overall outcome; technical feasibility and architecture concerns go to the Principal first.
Ensure the engagement follows global AI Studio delivery, architecture, engineering, security, responsible-AI, quality, documentation, and governance standards, escalating gaps or technical performance concerns to the appropriate leaders
Maintain an active understanding of client sentiment, stakeholder priorities, organizational changes, upcoming decisions, and emerging transformation needs beyond the immediate project plan.
Identify adjacent business problems, additional AI use cases, opportunities to scale successful solutions, and follow-on transformation needs; surface these to the AIDE so the Client Executive and AI GTM team can determine the appropriate pursuit strategy.
Support selected pre-sales, solution-shaping, transition, and proposal activities where delivery knowledge materially improves scope quality, feasibility, staffing, risk, commercial assumptions, or continuity into execution.
Coach and develop delivery team members, create clear accountability, recognize strong performance, address performance issues quickly, and foster a client-first, outcome-focused, high-performance culture.
Capture lessons learned, reusable delivery patterns, accelerators, and client insights that can improve future AI Studio engagements.