AI, Machine Learning Engineer

Hewlett Packard Enterprise

. Conduct research and stay current with advancements in AI and machine learning technologies, frameworks, and algorithms .

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 models and algorithms, with a strong foundation in mathematics and statistics. Proficient in deploying AI/ML solutions in production environments while ensuring scalability and performance.

Highest-signal resume keywords

Machine Learning AlgorithmsModel SelectionPython ProgrammingDeep LearningCloud Infrastructure

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningData PreprocessingFeature EngineeringHyperparameter TuningStatistical ModelingData MiningNatural Language ProcessingModel EvaluationProduction-Level Code DevelopmentData Visualization

Soft Skills

Strong CommunicationProblem-SolvingCritical ThinkingMentorshipCollaboration

Tools & Technologies

TensorFlowPyTorchScikit-learnGitHubAWSAzureLinuxGitHub CopilotN8NWindsurf

Industry Keywords

Artificial IntelligenceMachine LearningDeep LearningDevOpsSRE

Tech Stack

Tools & technologies

AWSAzureCloudJavaLinuxOpen SourcePythonPyTorchScikit-LearnTensorflow

About the role

Key responsibilities & impact

Conduct research and stay current with advancements in AI and machine learning technologies, frameworks, and algorithms
Collaborate with cross-functional teams to understand business requirements and design scalable, efficient, and robust AI/ML solutions
Develop, implement, and optimize machine learning models and algorithms, including 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-model performance, collect metrics, analyze results, and improve algorithms and system performance
Lead design review sessions and mentor junior and mid-level team members
Work with the engineering manager and team lead to set design and implementation standards
Lead meetings and foster a collaborative team environment
Provide technical leadership, mentorship, and guidance
Address and resolve challenges proactively
Develop and deliver strategic presentations and reports to senior stakeholders
Apply data mining, data modeling, natural language processing, and machine learning to extract and analyze information from 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
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 neural network architectures, transfer learning, generative models, and optimization algorithms
Experience conducting research and applying state-of-the-art AI and machine learning techniques
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 experience 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
Hybrid work arrangement