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

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

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.