AI and Machine Learning Engineer

Hewlett Packard Enterprise

. Lead AI/ML and innovation initiatives across HPE’s Hybrid Cloud portfolio .

Posted 10/27/2026full-timeAguadilla • Puerto RicoMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in AI and machine learning, focusing on model development, optimization, and deployment within cloud environments. Proficient in collaborating with cross-functional teams to deliver scalable solutions while mentoring junior team members.

Highest-signal resume keywords

Machine Learning AlgorithmsPython ProgrammingDeep Learning TechniquesCloud Infrastructure (AWS/Azure)Model Evaluation and Optimization

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningData PreprocessingFeature EngineeringModel SelectionHyperparameter TuningStatistical ModelingData MiningNatural Language ProcessingData VisualizationProduction-Level Code Development

Soft Skills

Excellent CommunicationProblem-SolvingCritical ThinkingTeam CollaborationMentoring

Tools & Technologies

TensorFlowPyTorchGitHubGitHub CopilotN8NVibe CodingWindsurfLinuxDevOpsSRE

Industry Keywords

AI/ML InitiativesHybrid Cloud PortfolioScalable SolutionsStatistical ConceptsCloud Infrastructure

Tech Stack

Tools & technologies

AWSAzureCloudJavaLinuxOpen SourcePythonPyTorchTensorflow

About the role

Key responsibilities & impact

Lead AI/ML and innovation initiatives across HPE’s Hybrid Cloud portfolio
Research and experiment with advanced AI and machine learning technologies, frameworks, and algorithms
Collaborate with cross-functional teams to understand requirements and design scalable, efficient, and robust AI/ML solutions
Develop, implement, optimize, and monitor machine learning models and algorithms
Perform data preprocessing, feature engineering, model selection, hyperparameter tuning, and large-dataset training
Deploy machine learning models into production environments with scalability, performance, and security considerations
Integrate models with existing software systems and infrastructure
Monitor deployed models, analyze metrics, fine-tune models, optimize algorithms, and improve system performance
Lead design review sessions and mentor junior and mid-level team members
Establish design and implementation standards with the engineering manager and team lead
Lead meetings and foster a collaborative team environment
Address and resolve technical challenges proactively
Deliver strategic presentations and reports to senior stakeholders with insights and recommendations
Apply data mining, data modeling, natural language processing, and machine learning to analyze datasets

Requirements

What you’ll need

Bachelor's or master’s degree in computer science, engineering, data science, machine learning, artificial intelligence, or closely related quantitative discipline
Typically, 4-7 years’ experience
Deep understanding of machine learning algorithms, including linear regression, decision trees, support vector machines, random forests, deep learning models, and reinforcement learning
Proficiency in model selection, hyperparameter tuning, and evaluating model performance using appropriate metrics
Strong foundation in mathematics and statistics, including linear algebra, calculus, probability theory, and statistical concepts
Proficiency in Python, R, or Java
Experience developing production-level code
Familiarity with software engineering best practices, version control systems such as GitHub, and software development methodologies
Strong understanding of GitHub Copilot, Cursor, N8N, vibe coding, Windsurf, and similar technologies
Experience in cloud infrastructure such as AWS or Azure
Knowledge of open source and Linux
Understanding of DevOps and SRE
Advanced knowledge and experience in deep learning, including convolutional neural networks, recurrent neural networks, transformers, transfer learning, generative models, and optimization algorithms
Experience conducting research and applying state-of-the-art AI and machine learning techniques
Excellent communication, problem-solving, and critical-thinking skills
Experience with machine learning frameworks and libraries such as TensorFlow, PyTorch, or sci-kit
Statistical modeling, data mining, and data visualization are highly preferred

Benefits

Comp & perks

Comprehensive benefits supporting physical, financial, and emotional wellbeing
Personal and professional development programs
Flexible work arrangements to manage work and personal needs
Inclusive workplace culture
Accommodation support during the application or interview process for applicants with disabilities