Senior Machine Learning Engineer

Responsibilities

• Lead the design, development, and production deployment of AI/ML and GenAI models for complex Digital Manufacturing use cases.
• Architect scalable, reliable ML systems and optimize end-to-end data and model pipelines.
• Collaborate closely with Digital Manufacturing teams and cross-functional stakeholders to define project objectives, success metrics, deliverables, and timelines.
• Establish and advocate best practices for ML engineering, MLOps, model governance, and system reliability.
• Mentor junior engineers and provide technical leadership across international teams.
• Stay current with state-of-the-art (SoTA) advancements in AI/ML and GenAI, adapting emerging technologies to real-world manufacturing challenges.

Required Qualifications

• Bachelor’s degree in engineering, Computer Science, or a related quantitative field.
• 10+ years of experience in ML Engineering or Applied AI, with demonstrated leadership on complex technical projects.
• Advanced proficiency in AI/ML frameworks and techniques (e.g., PyTorch, Scikit-learn, LangChain, etc.) and model deployment strategies.
• Strong understanding of MLOps, CI/CD pipelines for ML, and monitoring systems for model performance and drift detection.
• Hands-on experience with AWS services for ML model training, deployment, and scaling.
• Deep understanding of data pipelines, data quality principles, and working with large, unstructured datasets.
• Ability to navigate ambiguity, solve complex problems, and deliver high-quality, production-ready solutions.
• Excellent communication and collaboration skills with the ability to work independently and lead cross-functional, global teams.

Preferred / Nice to Have

• Master’s degree in engineering, Computer Science, or a related quantitative field.
• Experience with multi-cloud platforms (AWS, Azure, Google Cloud) for AI/ML solutions.
• Exposure to GenAI architectures (LLMs, RAG, embeddings, prompt engineering).
• Experience enforcing data quality standards, maintaining data dictionaries, and working with data governance frameworks.
  • Background or exposure to the semiconductor or manufacturing domain.