We are seeking a highly qualified AI Developer, with a Master’s or Ph.D. in Computer Science, Artificial Intelligence, Natural Language Processing (NLP), or a related field. 

The ideal candidate will have deep expertise in speech recognition, text analysis, and natural language processing, in addition to strong experience in machine learning, deep learning, and AI research. Proficiency in designing and deploying AI models into real-world applications is essential.  

Key Responsibilities:

·  Research, design, and implement AI models using machine learning and deep learning techniques, with a focus on speech recognition, NLP, and text understanding.

·  Develop and optimize speech-to-text, text-to-speech, and language processing pipelines using frameworks such as TensorFlow, PyTorch, Hugging Face Transformers, or similar.

·  Analyze large-scale audio and text datasets, preprocess data for linguistic models, and fine-tune models for real-world performance.

·  Build and deploy end-to-end AI solutions for automatic transcription, voice command understanding, and intelligent text analysis.

·  Collaborate with software engineers and product teams to integrate AI-powered language solutions into mobile and web applications.

·  Write technical documentation, publish research findings, and contribute to internal knowledge bases.

·  Stay current with advancements in speech processing, NLP, and AI, and recommend new tools and techniques for continuous improvement.

 Required Skills:

·  MSc or Ph.D. in Artificial Intelligence, Natural Language Processing, Speech Processing, or related fields.

·  Strong programming skills in Python, with hands-on experience in machine learning (ML) and deep learning (DL) frameworks such as TensorFlow, PyTorch, and Hugging Face.

·  Proven experience developing speech recognition (ASR), voice processing, or NLP models (e.g., sentiment analysis, text summarization, named entity recognition).

·  Experience with language model training, data pipelines, model validation, and deployment into production environments.

·  Proficiency in statistical analysis, linguistic data preprocessing, and performance tuning of AI models.

·  Familiarity with speech datasets (e.g., LibriSpeech, Common Voice) and text corpora.

·  Solid understanding of AI ethics, model explainability, and language model evaluation.