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Senior, specialized Staff ML/GenAI role with niche skills reduces competition.
Core ML/GenAI skills are transferable, but manufacturing/semiconductor domain preference increases sensitivity.
Explicit 9+ years plus specialized ML, GenAI, cloud, and MLOps stack enforces high strictness.
Own the end-to-end design, development, deployment, and operationalization of ML and GenAI models for manufacturing intelligence, supply chain analytics, and enterprise data platforms.
Architect and implement scalable data pipelines, feature stores, and model serving infrastructure using Lakehouse and cloud-native platforms like Databricks and Azure.
Lead prompt engineering, Retrieval-Augmented Generation pipelines, and agentic AI workflows while mentoring junior engineers and presenting strategic insights to senior leadership.
Master's degree in Computer Science, Data Science, Statistics, Mathematics, or related quantitative field.
9+ years professional experience in AI/ML engineering or related roles with minimum 3+ years in AI/ML solution architecture or enterprise AI implementation.
Strong expertise in ML algorithms, deep learning frameworks (PyTorch, TensorFlow), vector databases, semantic search, Python, distributed computing (Apache Spark), SQL, and MLOps practices including model versioning and monitoring.
Experience with cloud platforms (preferably Azure) and Lakehouse architectures (Databricks).
Experienced in translating complex business requirements into scalable, production-grade AI/ML solutions and communicating outcomes to non-technical and senior audiences.
Skilled in building and architecting agentic AI workflows and multi-LLM orchestration, with strong domain knowledge in manufacturing or semiconductor industry preferred.
Capable of mentoring engineers, establishing best practices in ML engineering, and driving AI initiatives with measurable business impact.