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:

• Architect and implement AI/ML models for automation, predictive analytics, and process optimization in manufacturing.
• Develop and maintain scalable data and ML pipelines; execute on end-to-end AI solution lifecycles (from data processing to model deployment and monitoring).
• Collaborate with product, engineering, and operations teams to design and integrate AI features into digital products.
• Apply Generative AI or LLMs for use cases in simulation, generative design, digital documentation, and intelligent operator assistance.
• Champion best MLOps practices, code quality, and testing for robust AI deployments.
• Mentor junior engineers and data scientists on technical best practices.
• Remain current with industry and AI advancements, and quickly adapt solutions to evolving needs.

Required Skills:

• Demonstrated expertise in Machine Learning, Deep Learning, Natural Language Processing (NLP), and/or Generative AI (LLMs, RAG, transformers).
• Experience deploying models with frameworks like TensorFlow, PyTorch, Hugging Face Transformers, etc.
• Strong programming skills in Python; familiarity with other languages or tools (Java, Go, Scala, Docker, K8s) is a plus.
• Experience with model training, fine-tuning, prompt engineering, and use of vector databases.
• Familiarity with cloud services (AWS/GCP/Azure) for model deployment.
• Experience with MLOps, CI/CD for ML, and version control (Git).

Nice to Have Skills:

• Experience with or exposure to digital twin tech in manufacturing (e.g., Nvidia Omniverse, Siemens, PTC); participate in integration/consumption rather than full-scale digital twin architecture.
• Knowledge of manufacturing data/standards (IoT, PLCs, MES/ERP connectivity).