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

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

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