Senior Data Scientist

Responsibilities:

• Design, implement, and optimize LLM pipelines for NLP tasks such as summarization, classification, and entity recognition.
• Build and deploy AI models to production with scalable, testable code.
• Develop and fine-tune models using domain-specific datasets, integrating them into live systems.
• Create retrieval-augmented systems using vector stores and embeddings for contextual Q&A.
• Support ASR-based NLP systems with diarization, tagging, and post-processing.
• Work closely with senior leadership to ensure technical alignment with product goals.
• Contribute to AI/ML best practices and help standardize development workflows across teams.

Primary Skills (Must have):

• Strong Python development with OOP principles and ML/AI best practices.
• Expertise in machine learning, deep learning, and NLP using libraries like scikit-learn, PyTorch, TensorFlow.
• Practical experience with LLM fine-tuning, LoRA, PEFT, and embedding-based models.
• Experience building RAG pipelines for question-answering, search, and knowledge summarization.
• Hands-on with vector stores (FAISS, Pinecone, ChromaDB), transformers, and Hugging Face models.
• Experience deploying models via FastAPI, Flask, and Docker.
• Good knowledge of speech-to-text tools like Whisper and AWS/GCP STT for transcription.
• Familiarity with prompt engineering, LangChain, and retriever models.
• Experience with cloud ML platforms (AWS, Azure, GCP) for model training and inference.
• Exposure to MLOps practices like versioning, monitoring, and automated retraining.

Secondary Skills

• Domain knowledge in financial services, market places, healthcare, pharma and life sciences NLP use cases.
• Experience with knowledge graphs, graph neural networks, and embeddings-based reasoning.
• Understanding of privacy-preserving ML (e.g., differential privacy, federated learning).
• Exposure to LangChain, LlamaIndex, Haystack, or other GenAI orchestration tools.