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Tier-1 brand, mid-level ML role, metro location, and broad GenAI skillset increase applicant competition.
Core ML engineering skills transfer across industries, though semiconductor domain experience is advantageous.
Mandatory 5+ years plus extensive ML, MLOps, cloud, and LLM requirements raise strictness.
Own end-to-end AI/ML project lifecycle including problem framing, data strategy, model development, deployment, and monitoring across enterprise applications.
Design and deploy machine learning and GenAI models focused on manufacturing intelligence, yield optimization, supply chain analytics, leveraging Lakehouse and cloud-native infrastructures (Databricks, Azure).
Lead implementation of advanced AI techniques such as prompt engineering for LLM optimization, Retrieval-Augmented Generation pipelines, agentic workflows, and mentor junior engineers while collaborating on business impact metrics.
Master's degree in Computer Science, Data Science, Statistics, Mathematics, or related quantitative field.
Minimum 5 years professional experience in AI/ML engineering or data science with proven production ML system ownership.
Technical skills required: machine learning algorithms, deep learning (PyTorch/TensorFlow), Python, Apache Spark, SQL, MLOps practices including MLflow, CI/CD, monitoring, drift detection.
Experience with cloud platforms (Azure preferred) and Lakehouse architectures (Databricks).
Experienced in deploying enterprise-grade AI/ML solutions directly linked to business KPIs and cross-functional stakeholder collaboration.
Skilled in advanced GenAI techniques like prompt engineering, LLM optimization, semantic search with vector DBs, and orchestration of multi-agent AI workflows.
Domain familiarity with semiconductor manufacturing or hardware/electronics sectors and preferably certified in cloud architectures (Azure, AWS, or GCP).