Physical AI Engineering Consultant - Manager - Consulting - Open Location

About the role

The Artificial Intelligence and Data team at EY helps apply cutting-edge technology and techniques to bring solutions to our clients. As part of this team, you'll work closely with clients and diverse teams from EY to create well-rounded approaches to advising and solving challenging problems.

Responsibilities

• Research, build, and implement scalable artificial intelligence systems that learn and make predictions tailored to business requirements across various environments, including cloud and on-premises.
• Enhance data pipelines and storage, ensuring data is clean, accurate, and optimized for XOps processes.
• Monitor and evaluate learning processes to continuously improve high-performance models, collaborating with other data and analytics professionals to industrialize analysis into effective analytics solutions.
• Contribute significantly to the delivery of innovative AI solutions.
• Work with a wide variety of clients to deliver the latest data science and big data technologies.
• Design and build scalable solutions that unify, enrich, and derive insights from varied data sources across a broad technology landscape.
• Guide clients through the complexities of modern robotics, digital twin applications, and software engineering.
• Provide technical leadership and perform development tasks to ensure robust engineering and maintenance of physical AI solutions that meet the ongoing business needs of our clients.

Requirements

Minimum of 5+ years of full-time working experience in Robotics, Digital Twin, and Computer Vision/Deep Learning/Reinforcement Learning.

Proficiency in programming languages such as Python, C++, or Java, with experience in robotics frameworks (e.g., ROS) and simulation environments.

Experience designing, building, and maintaining robotics systems and digital twin models.

Hands-on experience with NVIDIA Omniverse or similar simulation environments for robotics and digital twin applications.

Understanding of robotic systems, kinematics, dynamics, and control algorithms.

Understanding of various sensors (e.g., LIDAR, cameras) and actuators used in robotic systems.

Familiarity with creating and managing digital twins, including modeling, simulation, and real-time data integration.

Knowledge of machine learning techniques and algorithms, particularly in the context of robotics and automation.

Experience using data manipulation and analysis tools (Pandas, NumPy) to derive insights from sensor data and simulations, and experience with popular ML packages such as TensorFlow, PyTorch, or similar libraries.

Extensive experience using DevOps tools like GIT, Azure Devops and Agile tools such as Jira to develop and deploy analytical solutions with multiple features, pipelines, and releases.

Experience designing, building, and maintaining ML models, frameworks, and pipelines.

Experience designing and deploying end-to-end ML workflows on at least one major cloud computing platform.

Solid understanding of Machine Learning (ML) workflows including ingesting, analyzing, transforming data and evaluating results to make meaningful predictions.

Experience with MLOps methods and platforms such as MLFlow.

Experience with CI/CD practices to automate the testing and deployment of software in Software-in-the-loop (SIL) environments.

Experience with data structures, data modeling, and software engineering best practices.

Skills in technical design optimization.

Strong mathematical and quantitative skills including calculus, linear algebra, and statistics.

Willingness to travel to meet client obligations.

Effective communication skills to convey complex ideas effectively.

Qualifications

A Bachelor’s degree required (4-year degree). Focused on Business or Economics, Technology Entrepreneurship, Computer Science, Engineering, Informatics, Statistics, Applied Mathematics, Data Science, or Machine Learning.

Proven ability to develop solutions to complex problems and recommend changes to policies and procedures.

Strong judgment in selecting methods and techniques for obtaining results.

Experience in managing client relationships and delivering high-quality service.

Ability to lead teams effectively and manage change within the organization.

Strong analytical and decision-making skills to guide project direction.

Experience in project management and tracking deliverable completion.

Ability to build and maintain relationships with clients and team members.

Clearly communicate findings, recommendations, and opportunities to improve data systems and solutions.

Experience with containerization and scaling models.

Experience integrating models and feedback from downstream consumption systems - reporting and dashboards, AI driven applications.

Experience designing, building, and maintaining ML models, frameworks, and pipelines.

Experience designing and deploying end-to-end ML workflows on at least one major cloud computing platform.

Understand data structures, data modeling, and software engineering best practices.

Skills in technical design optimization.

Experience with Continuous Integration and Continuous Deployment/Deployment.

Understanding of Natural Language Processing and Generation.

Knowledge in Image Processing and Analysis.

Skills in Speech and Audio Processing and Analysis.

Ability to scale models effectively.

Strong relationship-building skills.

Demonstrated client trust and value.

Effective communication skills with impact.

Digital fluency and emotional agility.

Experience in hybrid collaboration.

Commercial acumen and negotiation skills.

Proven ability to lead teams and manage change.

Experience in managing complex projects with multiple stakeholders.

A strong understanding of industry trends and emerging technologies.

Skills in data visualization and storytelling with data.

A deep understanding of and ability to teach concepts, tools, features, functions, and benefits of different approaches to apply them.

Advanced degree in Robotics, Computer Science, Mechanical/Aerospace Engineering, Electrical Engineering, or other quantitative field; graduate school years can act as a substitute for some of the industry years requirement.

Previous experience in research projects related to robotics, automation, or digital twin technologies (including academic research), with contributions to academic journals or conferences, showcasing research findings and technical expertise.

Experience with computer vision techniques and libraries (e.g., OpenCV) for object detection, tracking, and recognition.

Proficiency in designing and implementing control systems for robotic applications, including PID controllers, state-space control, and adaptive control techniques.

Experience with Hardware-in-the-loop (HIL) testing techniques to validate the performance of robotic systems in real-time by integrating hardware components with simulation models.

Knowledge of Model-Based Systems Engineering (MBSE) methodologies to support the design, analysis, and verification of complex systems.

Familiarity with industry standards and regulations related to robotics and AI, ensuring that solutions meet safety and compliance requirements.

Experience with testing methodologies, including unit testing, integration testing, and system testing, to ensure the reliability and robustness of physical AI solutions.

Strong understanding of and/or interest in Agentic AI/Generative AI.

Knowledge of sustainability practices in technology.

A deep understanding of and ability to teach concepts, tools, features, functions, and benefits of different approaches to apply them.

Strong skills in languages beyond Python: R, JavaScript, Java, C++, C.

Experience fine-tuning Generative AI models.