**Company Description** **SanDisk** is a leading global provider of **flash memory and solid-state storage solutions** , designing and manufacturing products such as **SSDs, memory cards, and USB flash drives** for consumer, mobile, and enterprise applications. Founded in **1988** , the company has been a pioneer in flash technology, including the creation of the **first flash-based SSD in 1991** . Formerly part of Western Digital (2016-2025), SanDisk re-emerged as an **independent publicly traded company in 2025** , strengthening its focus on next-generation storage technologies. It remains one of the **world's largest suppliers of NAND flash memory** **Job Description** **Role Overview** We are looking for a highly skilled Machine Learning Engineer who can design, build, and own end-to-end ML systems in production. This role requires a strong blend of machine learning expertise, backend engineering, and full-stack development, with a focus on building reliable, scalable platforms used by leadership and critical business functions. **Key Responsibilities** + Design, develop, and maintain **end-to-end machine learning pipelines** , including data ingestion, training, evaluation, deployment, monitoring, and retraining. + Build and own **production-grade ML services** that are reliable, scalable, and fault-tolerant. + Architect and manage **async workflows and API-driven systems** for ML and data services. + Integrate ML solutions into **complex production environments** and distributed systems. + Design robust systems with a strong focus on **failure modes, observability, and guardrails** to ensure reliability.



+ Develop internal analytical tools used by **leadership and cross-functional teams** for decision-making. + Develop **interactive internal ML tools and dashboards using Streamlit** for model insights, monitoring, and experimentation. + Experience with cloud platforms (AWS, GCP, Azure). + Collaborate with data scientists and stakeholders to deliver impactful solutions. **Required Skills & Qualifications** **Core Engineering Skills** + Robust proficiency in **Python** , **SQL** , and building **RESTful APIs** + Experience with **asynchronous programming and workflows** + Solid understanding of **software engineering best practices** : Version control ( **bitbucket** ), Unit and integration testing, Code quality and maintainability **Machine Learning & MLOps** + Build or integrate **data ingestion pipelines** (batch or streaming) + Experience in performing EDA and understand the analysis. + Proven experience managing the **full ML lifecycle** . + Hands-on experience with **MLOps practices and tools** : + Experiment tracking + Model versioning + Automated training and deployment pipelines + CI/CD for ML systems **Systems,



Infrastructure & Orchestration** + Experience building **scalable and reliable ML systems in production** + Familiarity with: + **Containerization** (Docker) + **Orchestration platforms** (e.g., Kubernetes, Airflow, Prefect, Dagster) + **Infrastructure as Code (IaC)** + Experience with **distributed data processing systems** (e.g., Spark) + Understanding of **workflow orchestration and scheduling for ML pipelines** **Full Stack Development** + Experience developing **end-to-end applications** , including: + Backend pipelines and services + Frontend/UI components + Hands-on experience building **internal ML dashboards and tools using Streamlit** + Ability to create **intuitive interfaces** for monitoring models, exploring data, and enabling stakeholder interaction **Qualifications** **Required Qualifications** + Master's or PhD in Statistics, Data Science, Computer Science, or a related quantitative field. + 3-4+ years of experience in data science or machine learning pipeline. + Strong expertise in statistical analysis and machine learning techniques. + Proficiency in: + Python (pandas, numpy, scikit-learn, statsmodels) + SQL + Data visualization tools + Experience working with large-scale operational datasets. **Preferred Qualifications** + Experience working with Databricks or AzureML. + Familiarity with big data technologies (Spark, PySpark). + Experience working with cloud platforms (AWS, Azure, or GCP). + Knowledge of MLOps practices and model deployment frameworks. **Additional Information** All your information will be kept confidential according to EEO guidelines.

📌 Senior Engineer, Machine Learning (Bengaluru)
🏢 Sandisk
📍 Bengaluru