Data and AI Engineer

At SANDISK, our vision is to power global innovation and push the boundaries of technology to make what you thought was once impossible, possible.

At our core, SANDISK is a company of problem solvers. People achieve extraordinary things given the right technology. For decades, we’ve been doing just that. Our technology helped people put a man on the moon.

We are a key partner to some of the largest and highest growth organizations in the world. From energizing the most competitive gaming platforms, to enabling systems to make cities safer and cars smarter and more connected, to powering the data centers behind many of the world’s biggest companies and public cloud, SANDISK is fueling a brighter, smarter future.

Binge-watch any shows, use social media or shop online lately? You’ll find SANDISK supporting the storage infrastructure behind many of these platforms. And, that flash memory card that captures and preserves your most precious moments? That’s us, too.

We offer an expansive portfolio of technologies, storage devices and platforms for business and consumers alike.

Today’s exceptional challenges require your unique skills. It’s You & SANDISK. Together, we’re the next BIG thing in data

Responsibilities

Design, build, and maintain scalable ETL/ELT pipelines to ingest structured and unstructured data from SAP S/4HANA, Oracle, Salesforce, and third-party APIs into enterprise data lakes/lakehouses.; Develop and optimize data products (medallion architecture) on platforms like Databricks; Implement data quality, lineage, and governance controls using tools such as Purview, Alation, or Unity Catalog.; Build, deploy, and monitor ML models (classification, regression, forecasting, anomaly detection) using Python, PySpark, scikit-learn, TensorFlow, or PyTorch.; Develop GenAI and LLM-powered solutions — including RAG (Retrieval-Augmented Generation) pipelines, embeddings, vector databases (Pinecone, Azure AI Search), and prompt engineering for Copilot/Azure OpenAI/Bedrock.; Operationalize models through MLOps practices — CI/CD for ML, model versioning, drift monitoring, and automated retraining (MLflow, Azure ML, SageMaker, Vertex AI); Deploy and manage data/AI workloads on Azure, AWS, or GCP , leveraging services like ADF, Databricks, Glue, Lambda, Functions, and Kubernetes.

Requirements

4–6 years of professional experience in data engineering, ML engineering, or applied AI roles.; Proven track record of delivering at least 2–3 production-grade data or AI projects end-to-end; Working in global team ecosystem is a plus

Nice to have

Experience with enterprise ERP data (SAP S/4HANA, Oracle) and integration patterns (CDC, IDoc, OData, APIs).; Exposure to supply chain, procurement, Logistics analytics use cases (spend, demand forecasting, supplier risk).; Familiarity with Copilot Studio, or Agentic AI frameworks .