AI and Machine Learning Engineer

Location: Hybrid, with an average requirement of 2 days per week from an HPE office in Aguadilla, Puerto Rico or San Juan, Puerto Rico.

Company

hirify.global is a global edge-to-cloud company developing solutions that connect, protect, analyze, and act on data and applications.

What you will do

• Research current AI and machine learning technologies, frameworks, and algorithms and apply emerging techniques to complex problems.
• Design, develop, implement, and optimize machine learning models, including data preprocessing, feature engineering, model selection, hyperparameter tuning, and training on large datasets.
• Deploy models into production, integrate them with existing software and infrastructure, and address scalability, performance, security, and interoperability.
• Monitor deployed models, analyze metrics, fine-tune algorithms, and improve model performance and system quality.
• Lead design reviews, establish design and implementation standards with engineering leadership, and mentor junior and mid-level team members.
• Present technical and business recommendations to senior stakeholders and apply data mining, data modeling, natural language processing, and machine learning to analyze datasets.

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, neural networks, deep learning models, and reinforcement learning, as well as model selection, hyperparameter tuning, and performance evaluation.
• Strong foundation in mathematics and statistics, including linear algebra, calculus, probability theory, statistical concepts, and development of complex machine learning models.
• Strong programming skills in Python, R, or Java, with experience developing production-level code and using 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 convolutional neural networks, recurrent neural networks, transformers, transfer learning, generative models, and optimization algorithms.
• Experience conducting AI and machine learning research, exploring emerging technologies, and identifying opportunities to apply state-of-the-art techniques.
• Excellent communication, problem-solving, and critical-thinking skills for collaboration with cross-functional teams and stakeholders and for guiding complex technical decisions.
• Experience with popular machine learning frameworks and libraries such as TensorFlow, PyTorch, or scikit-learn.

Nice to have

• Knowledge of machine learning libraries and frameworks such as TensorFlow, PyTorch, scikit-learn, and Keras.
• Deep understanding of statistical modeling, data mining, and data visualization.

Culture & Benefits

• Health and wellbeing benefits supporting physical, financial, and emotional wellbeing.
• Personal and professional development programs supporting career growth and knowledge specialization.
• Inclusive workplace focused on varied backgrounds, individual uniqueness, and flexibility in managing work and personal needs.
• Accessibility accommodations are available during the application and interview process for candidates with disabilities.