Requirements

• Proficiency with machine learning and deep learning frameworks such as TensorFlow, PyTorch, scikit-learn, and Keras, along with collaboration tools like Jupyter Notebook and VS Code.
• Expertise in Natural Language Processing (NLP) tasks, including part-of-speech tagging, named entity recognition (NER), text parsing, and Optical Character Recognition (OCR). Experience working with unstructured data, including text, image, and audio data.
• Large Language Models (LLMs): Understanding of large language models (LLMs) such as GPT, BERT, or T5 including experience with prompt engineering, fine-tuning, and working with transformer-based architectures.
• Ability to quickly prototype ML solutions to validate use cases and drive innovation.
• Familiarity with public cloud platforms like Google Cloud Platform (GCP) or Amazon Web Services (AWS) for deploying and scaling ML solutions.
  
  

Good To Have

• Audio Processing Skills: Hands-on experience in audio signal processing, feature extraction, and analysis using libraries like LibROSA, PyDub, and SpeechRecognition.
• Familiarity with key NLP and audio processing libraries, such as Hugging Face Transformers, NLTK, SpaCy, and SpeechBrain.
• Understanding of MLOps principles, including model versioning, monitoring, and automated pipelines for scalable deployment.
  
  

This job was posted by Sushil Kumar from Carnera Technologies.