As part of this initiative, you’ll contribute to:

• System Integration: Seamlessly connecting diverse manufacturing and supply systems, data sources, and workflows into a unified digital ecosystem.
• Data-Driven Decision Making: Harnessing real-time data collection, analysis, and visualization to deliver actionable insights and operational intelligence.
• Automation & Optimization: Driving efficiency through intelligent scheduling, predictive maintenance, and quality control—without replacing core transactional systems.
• Enhanced Collaboration: Enabling transparent communication and coordination across teams, functions, and geographies.

If you're passionate about digital platforms, industrial innovation, and working with cutting-edge technologies—this is your opportunity to make a meaningful impact.

Key Responsibilities:

• Lead architectural design and development of digital twin-driven generative/optimization models for process simulation, process scheduling, and predictive analytics in manufacturing.
• Develop modular, reusable Omniverse extensions; orchestrate pipelines integrating synthetic data generation, model training, validation, and deployment.
• Implement advanced optimization: multi-objective, non-linear, evolutionary, and constraint-based approaches for process efficiency (e.g., scheduling, logistics, yield predictions).
• Guide the integration of embedded AI & Gen AI solutions with real-time industrial data streams, ensuring high-throughput, high-availability, and low-latency deployment.
• Mentor teams in coding best practices, simulation-modelling, and deployment workflows using OpenUSD, Omniverse, and latest industrial AI stacks.

Required Skills:

• Strong experience with Generative AI models. Hands-on experience with Amazon bedrock and AWS AI-ML services
• Proficiency in building and managing Agentic AI systems using frameworks like LangChain, AutoGen or similar.
• Expertise on cloud-native architecture, LLMOps and MLOps tools.
• Familiarity with security, cost and governance best practices for Gen AI on cloud environments.

Nice to Have Skills:

• Previous leadership in deploying ML pipelines and automation (CI/CD, containerization) at scale.
• Knowledge of quantization-aware training, model pruning, and NVIDIA-specific deployment tools (TensorRT, DeepStream SDK).
• Working knowledge of OpenUSD scene graphs, data schemas, and extension development for Omniverse Kit/Composer.
• Published research, open-source contributions, or patents in simulation-driven manufacturing AI or optimization.
• Familiarity with NVIDIA’s AI stack— RAPIDS, Triton Inference Server, TensorRT, cuOpt (or similar)
• Applied industrial AI expertise: root-cause analysis, predictive maintenance, digital twin calibration.