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 • United StatesMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in developing and optimizing machine learning algorithms and models, with a strong foundation in mathematics and statistics. Proficient in programming languages such as Python, R, or Java, and experienced in deploying AI/ML solutions within cloud infrastructures like AWS and Azure.

Highest-signal resume keywords

Machine Learning AlgorithmsDeep Learning TechnologiesPython ProgrammingCloud Infrastructure (AWS, Azure)Statistical Modeling

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningDeep LearningStatistical ModelingData MiningData VisualizationNatural Language ProcessingSoftware DevelopmentAlgorithm DevelopmentPerformance MetricsProduction-Level Code

Soft Skills

Strong CommunicationProblem-SolvingCritical ThinkingMentorshipCollaboration

Tools & Technologies

TensorFlowPyTorchScikit-learnGitHubGitHub CopilotCursorN8NVibe CodingWindsurfLinux

Industry Keywords

AI/MLHybrid CloudData ManagementSoftware Engineering Best PracticesDevOpsSREQuantitative DisciplineEmerging TechnologiesNeural Network ArchitecturesReinforcement Learning

Tech Stack

Tools & technologies

AWSAzureCloudJavaLinuxOpen SourcePythonPyTorchScikit-LearnTensorflow

About the role

Key responsibilities & impact

Lead AI/ML and innovation initiatives across HPE’s Hybrid Cloud portfolio
Develop and program integrated software algorithms for structured and unstructured data
Apply machine learning and statistical modeling to improve product and system performance, data management, quality, and accuracy
Formulate descriptive, diagnostic, predictive, and prescriptive insights and algorithms
Apply, optimize, and scale deep learning technologies and algorithms
Translate technical specifications into code
Document installation and maintenance procedures; complete programming, testing, and debugging
Define and monitor performance metrics
Translate customer requirements and industry trends into AI/ML products, solutions, and system improvement projects
Research advancements in AI/ML technologies, frameworks, and algorithms
Collaborate with cross-functional teams to design scalable, efficient, and robust AI/ML solutions
Develop, implement, optimize, and continuously improve machine learning models
Deploy models into production and integrate them with existing software systems and infrastructure
Monitor deployed models, analyze metrics, fine-tune models, and optimize system performance
Lead design review sessions and mentor junior and mid-level team members
Set design and implementation standards with the engineering manager and team lead
Lead meetings and foster a collaborative team environment
Provide technical leadership, mentorship, and guidance
Deliver strategic presentations and reports to senior stakeholders
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
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 and Azure
Knowledge of open source and Linux
Understanding of DevOps and SRE
Advanced knowledge and experience in deep learning, including neural network architectures, transfer learning, generative models, and optimization algorithms
Experience conducting research and exploring emerging AI and machine learning technologies
Strong communication, problem-solving, and critical-thinking abilities
Experience with machine learning frameworks and libraries such as TensorFlow, PyTorch, or scikit-learn
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
Reasonable accommodation support during the application or interview process