- 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
Technology->Generative AI->Generative AI for Data Analytics
