Staff AI/ML Engineer

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Skills you'll use

What you'll do

• Lead the end-to-end design, development, and deployment of machine learning models and GenAI solutions for manufacturing intelligence, yield optimization, supply chain analytics, and enterprise data platforms.
• Architect and implement data pipelines, feature stores, and model serving infrastructure on Lakehouse and cloud-native architectures (Databricks, Azure).
• Develop prompt engineering strategies and feedback loops for LLM optimization; capture and normalize LLM interactions into reusable Knowledge Artifacts.
• Implement Retrieval-Augmented Generation (RAG) pipelines, including document parsing, chunking, vectorization, and semantic search using embeddings and vector databases. Support domain-specific ontologies, taxonomies, governance and versioning.
• Architect Agentic Harness and Design workflows using industry-standard frameworks for autonomous task orchestration and multi-step reasoning, with safe and controlled execution via constraints, policies, and fallback paths.
• Collaborate with cross-functional teams to define KPIs, success metrics, and business impact measurements for AI/ML initiatives.
• Mentor intermediate and junior engineers; conduct code reviews and establish best practices for ML engineering.
• Present technical findings and strategic recommendations to senior leadership, distilling complex results into actionable business insights.
• Required Qualifications
• Master's degree in Computer Science, Data Science, Statistics, Mathematics, or a related quantitative field.

Who can apply

• Master's degree in Computer Science, Data Science, Statistics, Mathematics, or a related quantitative field.
• 9+ years of professional experience in AI/ML engineering, data science, or a related role, with demonstrated ownership of production ML systems and with at least 3+ years in AI/ML solution architecture or enterprise AI implementation.
• Deep expertise in machine learning algorithms, deep learning frameworks (PyTorch, TensorFlow), and statistical modeling.
• Expertise with vector databases, embedding models, and semantic search architectures.
• Strong proficiency in Python; experience with distributed computing frameworks (Apache Spark) and SQL.
• Hands-on experience with MLOps practices: model versioning (MLflow), CI/CD for ML, automated retraining, monitoring, and drift detection.
• Experience with cloud platforms (Azure preferred) and Lakehouse architectures (Databricks).
• Proven ability to translate business requirements into technical solutions and communicate results to non-technical stakeholders and senior leadership.

About Sandisk

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