AI and Machine Learning Engineer

Hewlett Packard Enterprise • Aguadilla, PR

1d • Hybrid

About The Position

Hewlett Packard Enterprise is seeking a Senior AI/ML & Innovation Engineer to lead initiatives across its Hybrid Cloud portfolio. The role develops and programs integrated software algorithms that structure, analyze, and leverage structured and unstructured data in product and systems applications. It applies machine learning, statistical modeling, and deep learning to improve product and system performance, data management, quality, and accuracy, translating technical specifications into code and customer needs and industry trends into AI/ML products, solutions, and system improvements.

The engineer researches emerging technologies and develops, tests, deploys, monitors, and improves machine learning models in production environments. The role includes selecting and architecting scalable, efficient, and robust solutions; integrating models with existing systems; analyzing performance metrics; and refining models and algorithms based on results. It also provides technical leadership through design reviews, mentoring, establishing standards with engineering leaders, leading meetings, and presenting strategic recommendations to senior stakeholders. The role is hybrid, requiring an average of two days per week from an HPE office.

Requirements

• Bachelor's or master's degree in computer science, engineering, data science, machine learning, artificial intelligence, or a closely related quantitative discipline.
• Typically 4–7 years of 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.
• Ability to understand and develop complex machine learning models and algorithms.
• Strong programming skills in Python, R, or Java.
• Experience developing production-level code and 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 with cloud infrastructure such as AWS or Azure.
• Knowledge of open source technologies and Linux.
• Understanding of DevOps and SRE.
• Advanced knowledge and experience in deep learning, including neural network architectures such as convolutional neural networks, recurrent neural networks, and transformers, as well as transfer learning, generative models, and deep-learning optimization algorithms.
• Experience conducting research, exploring emerging technologies, and identifying opportunities to apply state-of-the-art techniques.
• Excellent communication skills for effective collaboration with cross-functional teams and stakeholders.
• Strong problem-solving and critical-thinking abilities to guide projects, make strategic decisions, and address complex technical challenges.
• Experience with popular machine learning frameworks and libraries such as TensorFlow, PyTorch, or scikit-learn.

Nice To Haves

• Knowledge of libraries and frameworks such as TensorFlow, PyTorch, scikit-learn, and Keras is a plus.
• A deep understanding of statistical modeling, data mining, and data visualization is highly preferred.

Responsibilities

• Research and keep up to date with advancements in AI and machine learning technologies, frameworks, and algorithms; experiment with techniques to solve complex problems and improve models.
• Collaborate with cross-functional teams to understand business requirements and design AI and machine learning solutions.
• Select appropriate algorithms, models, and frameworks and architect systems for scalability, efficiency, and robustness.
• Develop, implement, and optimize machine learning models, including data preprocessing, feature engineering, model selection, hyperparameter tuning, and training on large datasets.
• Monitor and improve model performance and accuracy; deploy models to production, integrate them with existing systems, and account for scalability, performance, and security.
• Collect and analyze model metrics, identify areas for improvement, and fine-tune models and algorithms to enhance system performance.
• Organize and lead design review sessions, aligning discussions with project requirements and best practices.
• Mentor and provide feedback, technical guidance, and leadership to junior and mid-level team members.
• Work with the engineering manager and team lead to set design and implementation standards and support continuous improvement and project alignment.
• Lead meetings and foster a collaborative and productive team environment; proactively address and resolve challenges.
• Develop and deliver strategic presentations and reports to senior stakeholders, providing insights and recommendations.
• Apply data mining, data modeling, natural language processing, and machine learning to extract and analyze information from datasets.
• 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.

Benefits

• Comprehensive benefits supporting physical, financial, and emotional wellbeing.
• Career development programs to help employees pursue career goals and develop expertise.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior