GLO AI-ML specialist

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About the role

GLO AI-ML specialist This role has been designed as 'Hybrid' with a requirement that you will work on average 2 days per week from an HPE office. Who We Are: Hewlett Packard Enterprise is the global edge-to-cloud company advancing the way people live and work. We help companies connect, protect, analyze, and act on their data and applications wherever they live, from edge to cloud, so they can turn insights into outcomes at the speed required to thrive in today’s complex world. Our culture thrives on finding new and better ways to accelerate what’s next. We know varied backgrounds are valued and succeed here. We have the flexibility to manage our work and personal needs. We make bold moves, together, and are a force for good. If you are looking to stretch and grow your career our culture will embrace you. Open up opportunities with HPE. Job Description: HPE Operations is our innovative IT services organization. It provides the expertise to advise, integrate, and accelerate our customers’ outcomes from their digital transformation. Our teams collaborate to transform insight into innovation. In today’s fast paced, hybrid IT world, being at business speed means overcoming IT complexity to match the speed of actions to the speed of opportunities. Deploy the right technology to respond quickly to market possibilities. Join us and redefine what’s next for you. What you’ll do: Responsibilities: • Applies basic knowledge of the client's business need to formulate and define analytic objectives.  Uses available data elements, defines business rules, and solution objectives. • Develops, enhances and maintains a client's metadata based on analytic objectives.  May load data into the infrastructure, creates hypothesis matrix, and identifies available data to prepare for the Exploratory Data Anlysis (EDA) and hypotheses. • Builds models to supports/contribute to the overall solution, validates initial model and validates results & performance after the implementation. • Researches, identifies, and aids in delivering data science solutions to problem domain. Contributes significantly in measurement of business performance based on the model deployed. If needed, leads the model enhancements. • Create visualization of the model's insights for easy consumption. Machine Learning Model Development • Design, develop, train, evaluate, and deploy machine learning models for real-world business problems. • Implement supervised, unsupervised, reinforcement learning, and deep learning algorithms. • Perform feature engineering, feature selection, model tuning, and performance optimization. • Develop predictive, classification, forecasting, recommendation, anomaly detection, and optimization models. • Conduct model validation, statistical analysis, and performance benchmarking. Generative AI & Advanced AI Solutions • Develop and deploy LLM-based applications using Generative AI technologies. • Build Retrieval Augmented Generation (RAG) solutions using vector databases and embeddings. • Design prompt engineering frameworks and AI agents to automate business processes. • Fine-tune and optimize foundation models for domain-specific use cases. Deep Learning & NLP • Develop deep learning solutions using TensorFlow, PyTorch, and related frameworks. • Build Natural Language Processing (NLP) solutions including document intelligence, summarization, classification, sentiment analysis, semantic search, and conversational AI. • Apply transformer architectures, embeddings, and modern NLP techniques for advanced AI applications. MLOps & Production Engineering • Deploy machine learning models into production environments. • Implement model lifecycle management, model monitoring, automated retraining, and drift detection. • Build CI/CD pipeli

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