Senior AI/ML Engineer GenAI, LLM & RAG

Job Title: Senior AI/ML Engineer GenAI, LLM & RAG Experience: 12 - 15 Years Location: Hyderabad (Hybrid) Domain: Artificial Intelligence / Machine Learning / Generative AI About the Role We are looking for a highly skilled Senior AI/ML Engineer with strong expertise in Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and model fine-tuning techniques. The ideal candidate should have hands-on experience building, optimizing, and evaluating enterprise-scale GenAI solutions, with deep understanding of LLM architectures, fine-tuning methods, vector databases, and AI evaluation frameworks. Key Responsibilities Design, develop, and deploy GenAI and LLM-powered applications for enterprise use cases Build and optimize RAG pipelines including ingestion, chunking, embedding, retrieval, and response generation Fine-tune foundation models using PEFT techniques such as LoRA and qLoRA Work with prompt engineering, model optimization, and inference tuning Evaluate LLM and RAG system outputs using quantitative and qualitative metrics Implement and optimize Vector Database solutions for semantic search and retrieval Collaborate with Product, Engineering, and Data teams to productionize AI solutions Drive model experimentation, benchmarking, and performance improvements Ensure AI solutions meet scalability, reliability, governance, and security standards Required Skills & Experience 12 - 15 years of experience in AI/ML Engineering, Data Science, or NLP engineering Experience with cloud platforms: AWS / Azure / GCP Strong hands-on experience with LLMs, GenAI frameworks, and RAG systems Expertise in Fine-tuning methods: PEFT, LoRA, qLoRA LLM hyperparameters: temperature, top-p, top-k Evaluation metrics: ROUGE, BLEU, precision, recall, LLM-as-a-judge Strong knowledge of RAG architectures and chunking strategies: Fixed-size chunking Recursive chunking Semantic chunking Sliding window chunking Experience with Vector Databases such as: FAISS Pinecone Chroma Qdrant Weaviate Milvus Strong programming skills in Python Experience with ML/LLM frameworks Hugging Face LangChain LlamaIndex PyTorch / TensorFlow Good understanding of machine learning fundamentals and model lifecycle Job Title: Senior AI/ML Engineer GenAI, LLM & RAG Experience: 12 - 15 Years Location: Hyderabad (Hybrid) Domain: Artificial Intelligence / Machine Learning / Generative AI About the Role We are looking for a highly skilled Senior AI/ML Engineer with strong expertise in Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and model fine-tuning techniques. The ideal candidate should have hands-on experience building, optimizing, and evaluating enterprise-scale GenAI solutions, with deep understanding of LLM architectures, fine-tuning methods, vector databases, and AI evaluation frameworks. Key Responsibilities Design, develop, and deploy GenAI and LLM-powered applications for enterprise use cases Build and optimize RAG pipelines including ingestion, chunking, embedding, retrieval, and response generation Fine-tune foundation models using PEFT techniques such as LoRA and qLoRA Work with prompt engineering, model optimization, and inference tuning Evaluate LLM and RAG system outputs using quantitative and qualitative metrics Implement and optimize Vector Database solutions for semantic search and retrieval Collaborate with Product, Engineering, and Data teams to productionize AI solutions Drive model experimentation, benchmarking, and performance improvements Ensure AI solutions meet scalability, reliability, governance, and security standards Required Skills & Experience 12 - 15 years of experience in AI/ML Engineering, Data Science, or NLP engineering Experience with cloud platforms: AWS / Azure / GCP Strong hands-on experience with LLMs, GenAI frameworks, and RAG systems Expertise in Fine-tuning methods: PEFT, LoRA, qLoRA LLM hyperparameters: temperature, top-p, top-k Evaluation metrics: ROUGE, BLEU, precision, recall, LLM-as-a-judge Strong knowledge of RAG architectures and chunking strategies: Fixed-size chunking Recursive chunking Semantic chunking Sliding window chunking Experience with Vector Databases such as: FAISS Pinecone Chroma Qdrant Weaviate Milvus Strong programming skills in Python Experience with ML/LLM frameworks Hugging Face LangChain LlamaIndex PyTorch / TensorFlow Good understanding of machine learning fundamentals and model lifecycle