Lead AI Engineer

Summary:

You will design, develop, and deploy innovative AI/ML solutions for manufacturing and industrial products. A strong hands-on engineering approach, technical mentorship, and the ability to take projects from conception to deployment are expected. Cross-functional collaboration is key, but the position carries less strategic oversight and talent development responsibility than a Principal role.

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.

Requirements:

• 5–10 years of hands-on AI/ML engineering (preferably in manufacturing, SaaS, or product companies).

Required Skills:

• Proven ability 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).
• Excellent communication and ability to work in a cross-functional team.

Preferred 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).
• Masters in relevant fields, open-source contributions, or research publications.

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