Data Scientist

About the Role

We are seeking a highly skilled Data Scientist Generative AI & LLMs to design, develop, and deploy cutting-edge AI solutions using Large Language Models and Generative AI techniques. The role focuses on building scalable, production-grade AI systems that deliver measurable business impact across multiple use cases.

Key Responsibilities

• Design, develop, fine-tune, and deploy LLM-based solutions for enterprise use cases such as text generation, summarization, classification, search, and conversational AI
• Apply prompt engineering, fine-tuning (PEFT/LoRA), and RAG (Retrieval-Augmented Generation) techniques to improve model performance
• Build and maintain end-to-end ML pipelines, including data preparation, training, evaluation, deployment, and monitoring
• Integrate LLM solutions with APIs, vector databases, and enterprise data sources
• Evaluate model performance, address bias, hallucinations, and implement responsible AI practices
• Collaborate with product, engineering, and business stakeholders to translate requirements into scalable GenAI solutions
• Optimize inference performance, cost, and latency for production environments

Required Qualifications

• 5+ years of experience in Data Science / Machine Learning, with hands-on experience in Generative AI and LLMs
• Strong proficiency in Python and ML/AI frameworks (PyTorch, TensorFlow, Hugging Face, LangChain, LlamaIndex)
• Experience working with foundation models (OpenAI, Anthropic, LLaMA, Mistral, Gemini, etc.)
• Solid understanding of NLP concepts, transformers, embeddings, and attention mechanisms
• Hands-on experience deploying models in production environments
• Strong SQL skills and experience working with large-scale datasets

Preferred / Good to Have

• Experience with vector databases (Pinecone, FAISS, Weaviate, Chroma)
• Exposure to cloud platforms (AWS, Azure, GCP) and MLOps tools
• Knowledge of LLM evaluation frameworks, observability, and monitoring
• Familiarity with agentic architectures, tool calling, and orchestration frameworks
• Experience with model governance, explainability, and responsible A