Storage AI Engineer

Design and build enterprise-scale AI and Generative AI solutions including large language model-based agents and workflow automation to enhance user productivity and business outcomes.

Implement and optimize Retrieval-Augmented Generation (RAG) architectures and fine-tune machine learning models for performance, accuracy, and safe production deployment.

Develop scalable APIs and platforms for AI model deployment, with responsibility for monitoring, governance, and driving innovation adoption across the organization.

Bachelor's or master's degree in computer science, engineering, or closely related quantitative discipline.

3-5 years of relevant work experience in AI/ML or software engineering roles.

Proficiency in Python, SQL, Java/Go; experience with AI/ML frameworks such as PyTorch and Scikit-learn; and knowledge of generative AI techniques including prompt engineering and fine-tuning.

Experience with public cloud platforms (AWS/Azure), Kubernetes, Docker, CI/CD, and MLops frameworks. Hybrid work model requiring about 2 days per week from HPE office.

Practical experience combining machine learning, generative AI, and cloud software engineering to deliver production-ready AI products.

Capability to drive innovation and implement advanced AI technologies such as RAG, vector databases, and agentic AI at scale within enterprise-grade storage-related applications.

Comfortable working in a hybrid model and collaborating cross-functionally with data scientists and engineering teams to integrate AI solutions into real-world storage platforms.