AI and Machine Learning Engineer

Location: Hybrid, with an expectation of working an average of 2 days per week from an hirify.global office.

Company

hirify.global is a global edge-to-cloud technology company developing infrastructure, networking, data, and AI-driven digital experiences.

What you will do

• Conduct advanced AI and machine learning research and identify opportunities to apply emerging techniques to complex business problems.
• Design and architect AI solutions based on business requirements, constraints, scalability, performance, and maintainability.
• Develop, test, debug, optimize, and scale machine learning and deep learning algorithms for product and system applications.
• Translate customer requirements and industry trends into AI/ML products, solutions, and system improvement projects.
• Collaborate with product managers, data scientists, business analysts, engineering leaders, and other cross-functional stakeholders.
• Evaluate third-party AI tools, facilitate design reviews, present technical findings, and contribute to continuous improvement.

Requirements

• Bachelor’s degree in computer science, engineering, data science, machine learning, artificial intelligence, or a closely related quantitative discipline.
• Typically 4–7 years of relevant experience.
• Deep knowledge of machine learning algorithms, model selection, hyperparameter tuning, and model evaluation metrics.
• Strong foundations in linear algebra, calculus, probability, statistics, and complex machine learning model development.
• Production-level programming experience with Python, R, or Java, plus software engineering practices and Git.
• Advanced deep learning knowledge, including neural network architectures, transfer learning, generative models, and optimization techniques.

Nice to have

• Master’s degree in a relevant discipline.
• Experience with TensorFlow, PyTorch, scikit-learn, or Keras.

Culture & Benefits

• Comprehensive health, financial, and emotional wellbeing benefits.
• Career development programs supporting specialization and movement across divisions.
• Inclusive workplace that values varied backgrounds and individual uniqueness.
• Flexibility to manage work and personal needs within the hybrid work model.