Gen AI LLMOps Engineer

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

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

Technical and Professional 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