Description

Build and optimize large-scale language models and generative AI systems for real-world applications.

Design and implement control mechanisms for AI research while maintaining high-performance ML architectures.

Responsibilities

• Fine-tune transformer-based NLP/NLU models, including BERT and GPT, for generation, sentiment analysis, and entity recognition.
• Develop generative AI models leveraging LLMs for text understanding and generation tasks.
• Preprocess and curate text data through cleaning, tokenization, and augmentation.
• Optimize model performance and accuracy using relevant NLP metrics.
• Design APIs and automate ML/Ops workflows with CI/CD processes.

Required Skills

• 5+ years of experience in NLP/NLU with a focus on deep learning and open-source LLMs like Llama 2 and Falcon.
• Proficiency in Python.
• Hands-on experience with PyTorch and TensorFlow.
• Extensive work with NLP libraries including spaCy, NLTK, and Hugging Face Transformers.
• Experience with Langchain, Embedchain, and Vector Databases such as Pinecone, FAISS, Milvus, ChromaDB, or Vespa.
• Strong understanding of vector stores, search algorithms, and MLOps.
• Direct engineering experience with high-performance, large-scale ML systems.
• Familiarity with cloud computing platforms for model training and deployment.

Preferred Skills

• Experience maintaining or contributing to bug bounty and responsible disclosure programs.
• Active contributions to open-source projects.

Key Skills

Education

Any Graduate