Gen AI LLMOps Engineer

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 / Gen

AI Pipeline Development:

Design and implement end-to-end LLM pipelines:

Prompt engineering
Retrieval-Augmented Generation (RAG)
Fine-tuning / embeddings

Work with frameworks like:

Lang Chain, Lang Graph, Llama Index

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:

Lang Chain / Lang Graph / Llama Index

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:

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

Preferred skills: Technology->Generative AI->Generative AI for Data Analytics