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

Preferred Skills

• Generative AI for Data Analytics

Educational Requirements

MCA,MSc,MTech,Bachelor of Engineering,BCA,BSc,BTech