GLO AI-ML specialist

We are seeking a GLO AI-ML specialist to join our team. As a GLO AI-ML specialist, you will work on average 2 days per week from an HPE office.

Job Description:

HPE Operations is our innovative IT services organization that provides expertise to advise, integrate, and accelerate our customers' outcomes from their digital transformation. Our teams collaborate to transform insight into innovation.

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 Analysis (EDA) and hypotheses.
• Builds models to support/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.