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. 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 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