Description
We are seeking a Storage AI Expert to join our Hybrid Cloud team at Hewlett Packard Enterprise. As a Storage AI Expert, you will architect and deliver enterprise-scale AI solutions, build complex agent ecosystems, and establish standards for AI reliability, governance, and performance.
You will work at the intersection of machine learning, generative AI, software engineering, and cloud technologies to build scalable AI-powered products and platforms. Your responsibilities will include:
- Designing and building production-grade AI and Generative AI solutions using large language models (LLMs)
- Developing AI agents, copilots, and workflow automation systems that improve user productivity and business outcomes
- Implementing Retrieval-Augmented Generation (RAG) architectures using vector databases and enterprise knowledge sources
- Fine-tuning, evaluating, and optimizing machine learning and foundation models for accuracy, performance, safety, and cost
- Building scalable APIs, services, and platforms to serve AI models in production environments
- Partnering with cross-functional teams to identify high-impact AI opportunities and translate business requirements into technical solutions
- Establishing monitoring, observability, and governance mechanisms to ensure responsible AI deployment
- Driving experimentation, innovation, and adoption of emerging AI technologies across the organization
- Mentoring engineers and promoting engineering excellence, code quality, and AI best practices
To be successful in this role, you will need:
- A bachelor's or master's degree in computer science, engineering, or a closely related quantitative discipline
- Typically, 9-12 years of experience
- Strong programming skills in languages such as Python, SQL, Java, and Go
- Experience with AI/ML technologies including PyTorch, Scikit-learn, and LangChain
- Knowledge of Generative AI technologies including GPT, Claude, Llama, and prompt engineering
- Experience with cloud and MLOps technologies including public cloud, Kubernetes, Docker, and MLflow
We offer a hybrid work environment, with an expectation of working 2 days per week from an HPE office.