HP Enterprise—GLO AI-ML specialist

Job description

Description: 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 pipelines for machine learning deployment and version control. - Ensure scalability, performance, reliability, and governance of ML systems. Data Science & Analytics - Analyze large-scale structured and unstructured datasets. - Develop data preparation, feature extraction, and transformation frameworks. - Apply statistical modeling and experimental techniques to solve business challenges. - Design A/B testing and model evaluation strategies. Research & Innovation - Stay current with emerging AI, Machine Learning, and Generative AI technologies. - Evaluate new algorithms, frameworks, and techniques to improve model performance. - Drive innovation by identifying opportunities to leverage AI across business functions. Required Skills Machine Learning - Strong understanding of Machine Learning algorithms and techniques. - Supervised and Unsupervised Learning - Ensemble Models - Regression and Classification - Time Series Forecasting - Clustering - Recommendation Systems - Optimization Techniques - Reinforcement Learning Deep Learning - Neural Networks - CNNs, RNNs, LSTMs - Transformers - Transfer Learning - Model Fine-Tuning - Computer Vision Models - Advanced Deep Learning Architectures Generative AI - Large Language Models (LLMs) - Prompt Engineering - Retrieval Augmented Generation (RAG) - Vector Databases - Embeddings - Agentic AI Frameworks - OpenAI, Hugging Face, LangChain, Semantic Kernel Programming & Data Science - Python (Mandatory) - SQL - Pandas - NumPy - Scikit-learn - Statistical Analysis - Data Manipulation and Preprocessing MLOps - MLflow - Docker - Kubernetes - CI/CD Pipelines - Airflow - Model Monitoring - Model Versioning - Experiment Tracking Cloud & Big Data - Azure Machine Learning - AWS SageMaker - Google Vertex AI - Databricks - Apache Spark - Distributed Computing What you need to bring Preferred Qualifications - Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or related field. - 3+ years of hands-on experience building production-grade ML solutions. - Experience developing enterprise AI copilots, intelligent agents, and GenAI applications. - Certifications in AI/ML, Cloud Platforms, or Data Science technologies. - Experience with Responsible AI, AI Governance, and Explainable AI frameworks. Key Competencies - Advanced Machine Learning Expertise - Deep Learning & AI Research Mindset - Strong Mathematical & Statistical Foundations - MLOps and Production Deployment Expertise - Problem Solving & Critical Thinking - Innovation & Continuous Learning - Stakeholder Collaboration - Technical Leadership - Business Acumen Accessibility HPE is committed to creating an inclusive and accessible workplace and encourages applications from all qualified individuals, including those with disabilities. If you believe you require accommodation during any stage of the application or interview process, please submit your request by completing our secure form linked here. Note: This option is reserved for applicants needing assistance/reasonable accommodation related to a disability. What We Can Offer You: Health & Wellbeing We strive to provide our team members and their loved ones with a comprehensive suite of benefits that supports their physical, financial and emotional wellbeing. Personal & Professional Development We also invest in your career because the better you are, the better we all are. We have specific programs catered to helping you reach any career goals you have — whether you want to become a knowledge expert in your field or apply your skills to another division. Unconditional Inclusion We are unconditionally inclusive in the way we work and celebrate individual uniqueness. 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. Let's Stay Connected: Follow @HPECareers on Instagram to see the latest on people, culture and tech at HPE. #india #operations Job: EngineeringJob Level: TCP_02 HPE is an Equal Employment Opportunity/ Veterans/Disabled/LGBT employer. We do not discriminate on the basis of race, gender, or any other protected category, and all decisions we make are made on the basis of qualifications, merit, and business need. Our goal is to be one global team that is representative of our customers, in an inclusive environment where we can continue to innovate and grow together. Please click here: Equal Employment Opportunity. Hewlett Packard Enterprise is EEO Protected Veteran/ Individual with Disabilities. HPE will comply with all applicable laws related to employer use of arrest and conviction records, including laws requiring employers to consider for employment qualified applicants with criminal histories. Recruitment Fraud Alert We have become aware of an increase in fraudulent recruitment activities in which individuals impersonate our company or authorized recruitment agencies to offer fake employment opportunities. These scams may occur through false websites, emails, social media, or chat-based applications and often aim to obtain personal information or money. Please note that Hewlett Packard Enterprise (HPE), its direct and indirect subsidiaries and affiliated companies, and its authorized recruitment agencies/vendors will never charge a candidate a registration fee, hiring fee, or any other fee in connection with its recruitment and hiring process. We also never request personal information such as back account details, Social Security numbers, or national IDs via social media or chat applications. All legitimate job opportunities will come through official company channels, and candidates are responsible for verifying the credentials of any third party claiming to represent the company. Any reliance on fraudulent communication is at the individual’s own risk, and HPE disclaims legal liability for any resulting damages. If you suspect recruitment fraud, do not share personal information or make any payments and report the incident to your local authorities immediately. Skills: machine learning, python, deep learning, generative ai, natural language processing, reinforcement learning, statistical analysis, sql, tensorflow, pytorch, mlops, cloud platforms, azure machine learning, aws sagemaker, google vertex ai, databricks, apache spark, distributed computing, pandas, numpy, scikit-learn, mlflow, docker, kubernetes, ci/cd pipelines, airflow, model monitoring, model versioning, experiment tracking, a/b testing