Physical AI Delivery Lead: Digital Twins & Robotics

Accenture; Austin (TX); On-site; USD 120,000 - 260,000; Full time

Job summary

Accenture is seeking a Physical AI Delivery and Solutioning Manager to lead client workstreams across digital twins, robotics AI, and industrial integration. You will guide multi-disciplinary teams from discovery through mobilization, implementation, and transition, ensuring value realization and strong stakeholder engagement.

The role requires 5+ years of delivery experience, consulting or systems integration background, and deep domain knowledge in robotics, AI, and industrial operations.

Qualifications

• 5+ years of professional experience, including at least 3 years in consulting, systems integration or client-facing delivery.
• Practical experience in two domains: robotics integration, industrial automation, digital twins, simulation, AI/ML deployment, computer vision, industrial data, edge/cloud or orchestration.
• Experience managing a project workstream or technical module with plan, team, quality, risk, dependencies and stakeholder outcomes.
• Experience translating operational challenges into measurable outcomes tied to throughput, productivity, quality, safety, or operating cost.
• Ability to review technical designs and communicate implications to non-technical stakeholders.
• Understanding of production-readiness practices, testing, change control, cybersecurity, safety in industrial environments.
• Experience working with manufacturing, warehousing, logistics, engineering, data-center, or life-sciences operations.
• Bachelor’s degree or equivalent (minimum 12 years) work experience.

Responsibilities

• Lead delivery workstreams across digital twins and simulation, robotics AI and training, robotic orchestration, industrial integration or related Physical AI domains.
• Own workplans, milestones, staffing, budget, quality, dependencies, risks, decisions and client communications for the assigned scope.
• Translate operational problems into requirements, use cases, target outcomes, delivery increments, acceptance criteria and value-realization measures.
• Contribute to solution design, estimates, staffing models, implementation roadmaps, proposals, statements of work and delivery mobilization.
• Coordinate engineers, architects, industry SMEs, data and AI teams, cloud and edge teams, OT specialists, cybersecurity teams and ecosystem partners.
• Provide design and delivery governance across architecture decisions, interfaces, testing, safety, OT security, responsible AI, human-in-the-loop controls and production readiness.
• Guide teams through simulation, integration, hardware-in-the-loop, user acceptance, site acceptance, commissioning and transition-to-support activities as applicable.
• Lead client workshops, playback sessions, steering updates, design reviews and executive decision discussions with business, operations, engineering, IT and OT stakeholders.
• Manage engagement financials, including estimates, budget, forecast, staffing, scope change, partner dependencies, and delivery margin.
• Manage changes in scope and priorities while protecting client outcomes, technical integrity, delivery economics and team sustainability.
• Identify follow-on opportunities grounded in demonstrated value and client needs and support shaping of the next phase.

Skills

Delivery management

Stakeholder management

Industrial automation

Robotics

Digital twins

AI/ML deployment

OT/ cybersecurity

Education

Bachelor’s degree or equivalent (12 years) work experience

Tools

ROS 2

Simulation tools

Digital twin platforms

Industrial automation

Cloud/edge platforms

Job description

Accenture is seeking a Physical AI Delivery and Solutioning Manager to lead client workstreams across digital twins, robotics AI, and industrial integration. You will guide multi-disciplinary teams from discovery through mobilization, implementation, and transition, ensuring value realization and strong stakeholder engagement.

The role requires 5+ years of delivery experience, consulting or systems integration background, and deep domain knowledge in robotics, AI, and industrial operations.