Deep Learning Engineer

Company Description

Role Description

Model Development and Delivery

• Collect and prepare datasets, select suitable architectures, and implement and train deep learning models.
• Evaluate model performance using relevant metrics and iterate based on feedback and new data.
• Deploy models to production environments, monitor performance, and optimize solutions.

Collaboration and Professional Practice

• Contribute to technical documentation and stay current with advances in deep learning.
• Support collaborative problem-solving across project teams and communicate technical concepts clearly.
• Contribute to client-focused solutions and work with stakeholders on practical AI applications.

Technical Qualifications

• Strong machine learning and deep learning foundation, including CNN, RNN, and Transformer architectures.
• Proficiency with Python, TensorFlow, PyTorch, and related AI libraries.
• Experience with data preprocessing, feature engineering, structured and unstructured data, and advanced analytics.
• Knowledge of APIs, MLOps, model deployment, optimization, and production performance tuning.
• Understanding of algorithms, statistics, and linear algebra relevant to AI and machine learning.

Education and Collaboration

• A bachelor's or master's degree in Computer Science, Data Science, Engineering, Mathematics, or a related field, or equivalent practical experience.
• Ability to collaborate across functions and communicate complex technical concepts clearly.
• AI consulting or direct client experience is a plus.
• Familiarity with AWS, Azure, GCP, Docker, or Kubernetes is advantageous.