Lead AI/ML Engineer

In this role as an experienced AI Engineer with a focus on Natural Language Understanding (NLU), you will be responsible for designing, developing, and deploying scalable AI solutions to enhance Conversational AI. Utilizing techniques such as LLMs, Retrieval-Augmented Generation (RAG), and prompt engineering, you will contribute to the advancement of intelligent products and services. Your primary tasks will involve owning the full lifecycle of model development, from data preparation to deployment in production environments. **Responsibilities:** - Design, fine-tune, and deploy LLM-based applications for Conversational AI use cases - Build scalable retrieval-augmented generation (RAG) pipelines integrating LLMs with structured/unstructured data sources - Develop prompt engineering strategies, templates, and evaluation frameworks for LLM-driven workflows - Collaborate with cross-functional teams to implement AI-driven solutions for business challenges - Optimize models for low-latency inference using techniques such as quantization, distillation, and ONNX, TensorRT - Establish robust data processing, labeling, and augmentation pipelines to enhance model performance - Implement monitoring and evaluation systems for deployed LLMs to ensure reliability, fairness, and safety - Stay updated on emerging trends in LLMs, retrieval systems, and generative AI frameworks **Requirements:** - 8+ years of hands-on experience in Natural Language Understanding (NLU) - Strong proficiency in Python, PyTorch, and related frameworks (e.g., Hugging Face Transformers, Sentence Transformers) - Proven track record of developing and deploying NLP or LLM pipelines at scale in production environments - Solid understanding of transformer architectures and attention mechanisms - Proficiency in utilizing LLM provider APIs like OpenAI, Gemini for prompt design, fine-tuning, and evaluation - Experience with model optimization techniques such as quantization, pruning, ONNX, TensorRT, or model distillation - Bachelors or Masters degree in Computer Science, Artificial Intelligence, or a related field **Nice to Have:** - Hands-on experience with RAG and vector databases (e.g., FAISS, Qdrant, pgVector) - Prior involvement in LLM fine-tuning, alignment, or evaluation - Familiarity with LLM orchestration frameworks like LlamaIndex or similar tools - Understanding of multi-provider LLM orchestration, integrating APIs from OpenAI, Gemini, etc., for fallbacks, routing, or ensemble strategies - Knowledge of MLOps for LLMs, including model serving and monitoring - Understanding of embedding models, context management, and token optimization for scalable LLM applications In this role as an experienced AI Engineer with a focus on Natural Language Understanding (NLU), you will be responsible for designing, developing, and deploying scalable AI solutions to enhance Conversational AI. Utilizing techniques such as LLMs, Retrieval-Augmented Generation (RAG), and prompt engineering, you will contribute to the advancement of intelligent products and services. Your primary tasks will involve owning the full lifecycle of model development, from data preparation to deployment in production environments. **Responsibilities:** - Design, fine-tune, and deploy LLM-based applications for Conversational AI use cases - Build scalable retrieval-augmented generation (RAG) pipelines integrating LLMs with structured/unstructured data sources - Develop prompt engineering strategies, templates, and evaluation frameworks for LLM-driven workflows - Collaborate with cross-functional teams to implement AI-driven solutions for business challenges - Optimize models for low-latency inference using techniques such as quantization, distillation, and ONNX, TensorRT - Establish robust data processing, labeling, and augmentation pipelines to enhance model performance - Implement monitoring and evaluation systems for deployed LLMs to ensure reliability, fairness, and safety - Stay updated on emerging trends in LLMs, retrieval systems, and generative AI frameworks **Requirements:** - 8+ years of hands-on experience in Natural Language Understanding (NLU) - Strong proficiency in Python, PyTorch, and related frameworks (e.g., Hugging Face Transformers, Sentence Transformers) - Proven track record of developing and deploying NLP or LLM pipelines at scale in production environments - Solid understanding of transformer architectures and attention mechanisms - Proficiency in utilizing LLM provider APIs like OpenAI, Gemini for prompt design, fine-tuning, and evaluation - Experience with model optimization techniques such as quantization, pruning, ONNX, TensorRT, or model distillation - Bachelors or Masters degree in Computer Science, Artificial Intelligence, or a related field **Nice to Have:** - Hands-on experience with RAG and vector databases (e.g., FAISS, Qdrant, pgVector) - Prior involvement in LLM fine-tuning, alignment, or evaluation - Familiarity with LLM orchestration framewor