Job Description: We are looking for a GenAI LLMOps Engineer to operationalize and scale Large Language Models LLMs and Generative AI applications in production environments This role focuses on LLM deployment orchestration monitoring and optimization ensuring reliable and efficient GenAI systems Key Responsibilities: LLM Deployment Productionization Deploy and manage LLMs OpenAI Llama Mistral etc in production environments Build scalable inference pipelines real time batch Integrate LLMs into applications via APIs and microservices LLMOps GenAI Pipeline Development Design and implement end to end LLM pipelines Prompt engineering Retrieval Augmented Generation RAG Fine tuning embeddings Work with frameworks like LangChain LangGraph LlamaIndex RAG Data Integration Build and optimize RAG pipelines using vector databases Work with tools like Pinecone FAISS Weaviate Chroma Handle document ingestion chunking indexing and retrieval Model Monitoring Optimization Monitor LLM performance Latency Accuracy hallucinations Cost efficiency Implement Prompt optimization Feedback loops Guardrails evaluation frameworks MLOps for LLMs Build CI CD pipelines for Model updates Prompt version control Manage experiment tracking and deployments Ensure reproducibility of LLM workflows Technical Requirements: Strong Python programming Hands on experience with LLMs Generative AI Experience with LangChain LangGraph LlamaIndex Solid understanding of RAG architecture Prompt engineering Embeddings vector search Experience building APIs using FastAPI Flask Additional Responsibilities: What This Role Is NOT Not pure Data Scientist model building only Platform Engineer infra heavy role Traditional MLOps without LLM exposure This role focuses on LLM deployment RAG GenAI pipelines Operationalizing GenAI applications Preferred Skills: Technology->Generative AI->Generative AI for Data Analytics