Physical AI Engineering Consultant - Manager - Consulting - Open Location

About the role

Join our Artificial Intelligence and Data team to help bring solutions to our clients. You'll work side-by-side 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.
• Lead workstream delivery and ensure effective management of processes and projects.
• Manage professional employees and supervise teams to deliver complex technical initiatives, with accountability for performance and results.
• Engage actively with clients, participate in daily working sessions, and lead workstreams from planning through execution to closure.
• Identify opportunities for additional services and manage engagement economics.
• Research and implement scalable AI systems that meet business requirements.
• Enhance data pipelines and storage for optimal data accuracy and cleanliness.
• Monitor and optimize learning processes to improve high-performance models.

Requirements

• 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.
• 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.
• Proficiency 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.
• A 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 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.
• A strong understanding of data structures, data modeling, and software engineering best practices.
• Proficiency using data manipulation tools and libraries such as SQL, Pandas, and Spark.
• Clearly communicate findings, recommendations, and opportunities to improve data systems and solutions.
• Experience with containerization and scaling models.
• Integrate models and feedback from downstream consumption systems - reporting and dashboards, AI-driven applications.
• Experience with machine learning algorithms and data architecture design.
• A strong understanding of and/or interest in Agentic AI/Generative AI.
• Knowledge of sustainability practices in technology.
• Strong mathematical and quantitative skills including calculus, linear algebra, and statistics.
• Willingness to travel to meet client obligations.
• Deep Learning expertise.
• 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.

Qualifications

• 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.
• Ability to develop and optimize algorithms for tasks such as path planning, motion planning, and decision-making in robotics.
• 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.
• Proficiency in 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.

Benefits

At EY, we offer a comprehensive compensation and benefits package where you’ll be rewarded based on your performance and recognized for your extraordinary talents in a diverse and inclusive culture of globally connected teams.

Pay

Competitive compensation and benefits package.

Schedule

Flexible schedule to accommodate client needs and personal commitments.