AI Research Engineer

Overview

We are seeking a motivated AI Research Engineer with a deep passion for Machine Learning (ML), Deep Learning, Natural Language Processing (NLP), and Reinforcement Learning to join our research-driven team. The ideal candidate will possess strong skills in frameworks like TensorFlow and PyTorch, advanced Python programming expertise, and a proven ability to innovate in the fields of Data Science and Artificial Intelligence. This role is ideal for candidates who enjoy pushing the boundaries of AI through cutting-edge research and development.

Key Responsibilities

• AI Research: Conduct advanced research in ML, Deep Learning, NLP, and Reinforcement Learning, contributing to the development of novel AI methodologies.
• Model Innovation: Design and prototype state-of-the-art machine learning models tailored for real-world applications.
• Framework Expertise: Utilize frameworks such as TensorFlow and PyTorch to build, train, and evaluate AI models.
• Algorithm Development: Develop and optimize algorithms for tasks like sequence modeling, decision-making, and predictive analytics.
• Experimentation: Perform experiments to validate hypotheses, improve existing techniques, and explore innovative approaches.
• Collaboration: Work closely with multidisciplinary teams to translate research outcomes into practical applications.
• Documentation and Publication: Document research findings thoroughly and contribute to publications in top AI and ML journals or conferences.
• Scalability: Design scalable AI solutions and assist in their integration into production environments.
• Learning and Development: Stay informed about the latest trends and breakthroughs in AI, incorporating them into research and development activities.

Key Requirements

• Academic Background:
• A strong foundation in computer science, mathematics, or related fields (Masters or Ph.D. preferred).
• Research experience in ML, Deep Learning, NLP, or Reinforcement Learning, demonstrated through projects or publications.
• Technical Skills:
• Proficiency in Python and libraries such as NumPy, Pandas, and Scikit-learn.
• Advanced knowledge of frameworks like TensorFlow and PyTorch for designing and implementing AI models.
• Strong understanding of NLP techniques (e.g., transformers, embeddings, text generation).
• Hands-on experience with Reinforcement Learning algorithms and applications.
• Mathematical Expertise: Solid understanding of linear algebra, calculus, probability, and optimization techniques.
• Problem-Solving: Ability to identify key challenges and develop innovative AI solutions.
• Team Collaboration: Strong communication skills with the ability to work effectively in a collaborative research environment.