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AI Engineer – Physical AI & Digital Twin Systems

ID de Puesto
511540
Publicado desde
26-Junio-2026
Organización
Siemens Energy
Ámbito de trabajo
Research & Development
Empresa
SIEMENS ENERGY INDIA LIMITED
Nivel de experiencia
Profesional Experimentado
Tipo de jornada
Jornada completa
Modalidad de trabajo
Oficina/Solo presencial
Tipo de contrato
Indefinido
Ubicación(es)
  • Bangalore - Karnataka - India
  • Gurugram - Haryana - India
  • Pune - Maharashtra - India
Title: AI Engineer – Physical AI & Digital Twin Systems

Position Summary:

Siemens Energy Gas Services (GS) seeks an AI Engineer to build production-grade Physical AI and Digital Twin systems for advanced manufacturing. The role centers on robot perception, reasoning, control, simulation, and deployment across cloud, edge, and real-world operations.
You will design, train, evaluate, and deploy AI models that enable autonomous and semi-autonomous robotic workflows across simulation and physical environments, owning the lifecycle from data and experimentation to inference, monitoring, and continuous improvement.
Working at the intersection of robotics, industrial software, and AI, you will partner with product, controls, robotics, and domain teams to deliver reliable, observable, and scalable systems that improve autonomy, safety, and productivity.

How You’ll Make an Impact (key responsibilities of role)

• Build and operate data and training pipelines for collection, labeling, augmentation, versioning, and evaluation across simulation and real-world datasets.
• Develop and deploy AI models for robot perception, reasoning, and action, including computer vision, multimodal models, and Vision-Language-Action (VLA) systems.
• Apply sim-to-real methods using synthetic data, domain randomization, imitation learning, transfer learning, and benchmark-driven validation.
• Integrate models with Digital Twin and robotics platforms such as NVIDIA Isaac Sim, Isaac Lab, robot middleware, and orchestration services.
• Deploy scalable inference and control services on cloud and edge environments using containers, Kubernetes, APIs, and event-driven workflows.
• Establish observability for model and system performance, including experiment tracking, evaluation, drift detection, telemetry, and safe rollback mechanisms.
• Collaborate with cross-functional teams to translate industrial use cases into production-ready AI capabilities with measurable reliability and productivity impact.

What You Bring (required qualification and skill sets)

• Bachelor’s or master’s degree in computer science, AI, Robotics, Electrical Engineering, or a related field.
• 2–5 years of experience building, training, evaluating, and deploying AI/ML systems in production.
• Strong Python engineering skills, including packaging, testing, APIs, and performance-oriented development; C++ is a plus for robotics integration.
• Hands-on experience with deep learning frameworks such as PyTorch and with computer vision or multimodal model development.
• Experience with robotic AI concepts such as perception, planning support, control integration, imitation learning, reinforcement learning, or Vision-Language-Action models.
• Practical knowledge of MLOps and deployment, including experiment tracking, model versioning, CI/CD, containers, and cloud or edge inference.
• Experience with simulation and Digital Twin workflows, including sim-to-real validation and evaluation using telemetry, sensor, and vision data.
• Ability to define metrics, benchmarks, and safety-minded validation processes for reliable deployment in industrial environments.
Good-to-Have Qualifications
• Experience with NVIDIA Isaac Sim, Isaac Lab, Omniverse, or similar robotics simulation platforms.
• Familiarity with world models, synthetic data generation, diffusion policy methods, or foundation-model-based robotics workflows.
• Exposure to edge AI platforms such as NVIDIA Jetson and to latency-, compute-, or power-constrained deployment.
• Understanding robot middleware, telemetry pipelines, and distributed or real-time robotic system architectures.
• Experience in manufacturing, industrial automation, or other safety-critical operational environments.

Key Attributes

• Strong problem solving mindset and ability to work with team members from different domains.
• Excellent communication skills and willingness to support others across the team.
• Hands‑on engineering mindset with focus on practical development & deployment.
• Ability to clearly communicate complex technical concepts to non‑experts.
• High ownership and accountability.
• Comfortable working across R&D, engineering, and service organizations.