Job
Description
Key Responsibilities: Design, develop, and train deep learning models for structured and unstructured data. Fine-tune pre-trained large language models (e.g., LLaMA, Mistral, Falcon, GPT-based models) for custom use cases. Evaluate model performance using standard metrics and improve based on experimentation. Build and maintain scalable training pipelines and inference systems. Collaborate with product and research teams to convert business requirements into technical solutions. Perform data preprocessing, augmentation, and annotation where needed. Optimize model inference speed and resource efficiency (e.g., quantization, pruning). Keep up with the latest research in ML/DL and apply relevant techniques to real-world problems. Required Qualifications: B.Tech / M.Tech / MS in Computer Science, Electrical Engineering, or related field. 23 years of hands-on experience in machine learning and deep learning. Strong knowledge of Python and frameworks such as PyTorch or TensorFlow. Proven experience training deep neural networks from scratch or via transfer learning. Exposure to LLMs – fine-tuning, prompt engineering, or training smaller transformer models. Familiarity with deployment of models using FastAPI, Flask, or similar. Preferred Qualifications: Experience with Hugging Face Transformers, LangChain, or similar frameworks. Knowledge of distributed training and GPU acceleration. Understanding of vector databases and RAG (retrieval-augmented generation) systems. Basic understanding of MLOps tools like Docker, Kubernetes, and CI/CD for ML workflows. Familiarity with common NLP tasks: text classification, summarization, NER, etc. What We Offer: A fast-paced, collaborative, and intellectually challenging environment. Opportunity to work on cutting-edge AI products and infrastructure. Flexible work options and a strong focus on work-life balance. Competitive compensation and equity options (if applicable).
