Lead AI/ML Engineer

Job Description:\nWe are seeking an experienced Lead AI/ML Engineer with a strong background in Natural Language Understanding (NLU) who is passionate about pushing the boundaries of Conversational AI. In this role, you will design, develop, and deploy scalable AI solutions leveraging LLMs, Retrieval-Augmented Generation (RAG), and prompt engineering techniques to power intelligent products and services.\nAs part of our ML/AI team, you’ll own the full lifecycle of model development — from data preparation and fine-tuning to inference optimization and deployment in production environments.\n\nResponsibilities:\nDesign, fine-tune, and deploy LLM-based applications for Conversational AI use cases\nBuild scalable retrieval-augmented generation (RAG) pipelines that combine LLMs with structured/unstructured data sources\nDevelop prompt engineering strategies, templates, and evaluation frameworks for LLM-driven workflows\nCollaborate with cross-functional teams to identify and implement AI-driven solutions to business problems\nOptimize models for low-latency inference using quantization, distillation, and other model optimization techniques (e.g., ONNX, TensorRT)\nBuild robust data processing, labeling, and augmentation pipelines to improve model performance\nImplement monitoring and evaluation systems for deployed LLMs, ensuring reliability, fairness, and safety\nStay current with emerging trends in LLMs, retrieval systems, and generative AI frameworks\n\nRequirements:\n8+ years of hands-on experience in NLU\nStrong proficiency in Python and PyTorch and related frameworks (like Hugging Face Transformers, Sentence Transformers etc.)\nProven experience developing and deploying NLP or LLM pipelines in production environments at scale\nSolid understanding of transformer architectures and attention mechanisms\nProficiency in using LLM provider APIs such as OpenAI, Gemini etc.including prompt design, fine-tuning, and evaluation\nExperience with model optimization techniques such as quantization, pruning, ONNX, TensorRT, or model distillation\nBachelor’s or Master’s degree in Computer Science, Artificial Intelligence, or a related field\n\nNice to Have:\nHands-on experience with RAG and vector databases (e.g., FAISS, Qdrant, pgVector etc.)\nPrior work on LLM fine-tuning, alignment, or evaluation\nExperience with LLM orchestration frameworks such as LlamaIndex or similar tools\nFamiliarity with multi-provider LLM orchestration, integrating APIs from OpenAI, Gemini etc. and others for fallbacks, routing, or ensemble strategies\nKnowledge of MLOps for LLMs, including model serving and monitoring\nUnderstanding of embedding models, context management, and token optimization for scalable LLM applications