LLM Engineer

Training Basket; Dadri; On-site; INR 1,000,000 - 2,000,000; Full time

Job summary

Training Basket is seeking an AI/NLP Engineer to develop large language model applications and RAG pipelines. You will implement prompt engineering, integrate LangChain or LlamaIndex, and optimize embeddings and retrieval systems.

You\'ll work with Python, REST APIs, and cloud platforms, collaborate with data scientists and software engineers, and deploy models for production with monitoring and cost optimization.

Qualifications

• Strong knowledge of Python programming.
• Understanding of Large Language Models (LLMs) and Generative AI.
• Knowledge of Natural Language Processing (NLP) and prompt engineering.
• Experience with LangChain, LlamaIndex, or similar frameworks.
• Understanding of RAG architecture, embeddings, and semantic search.
• Familiarity with vector databases such as FAISS, Pinecone, or Chroma.
• Knowledge of REST APIs and application integration.
• Basic understanding of machine learning and deep learning concepts.
• Familiarity with Git and cloud platforms such as AWS, Azure, or Google Cloud is an advantage.

Responsibilities

• Develop and optimize prompts to improve model accuracy.
• Build Retrieval-Augmented Generation (RAG) pipelines using vector databases and embeddings.
• Integrate LLMs with applications using APIs and frameworks such as LangChain or LlamaIndex.
• Work with vector databases for efficient semantic search and retrieval.
• Collaborate with data scientists, software engineers, and product teams to build AI-powered solutions.
• Deploy and monitor LLM applications in cloud environments.

Skills

Python

NLP

LLMs/Generative AI

Prompt engineering

REST APIs

Git

Cloud platforms

Tools

LangChain

LlamaIndex

FAISS

Pinecone

Chroma

Job description

Work with LLMs such as GPT, Llama, Gemini, and other open-source or commercial models.

Develop and optimize prompts to improve model accuracy and response quality.

Build Retrieval-Augmented Generation (RAG) pipelines using vector databases and embedding models.

Implement NLP techniques for text processing, summarization, classification, and information extraction.

Integrate LLMs with applications using APIs and frameworks such as LangChain or LlamaIndex.

Work with vector databases such as FAISS, Pinecone, Chroma, or Weaviate.

Evaluate model performance, reduce hallucinations, and optimize response latency and cost.

Collaborate with data scientists, software engineers, and product teams to build AI-powered solutions.

Deploy and monitor LLM applications in cloud environments.

Requirements

Strong knowledge of Python programming.

Understanding of Large Language Models (LLMs) and Generative AI.

Knowledge of Natural Language Processing (NLP) and prompt engineering.

Experience with LangChain, LlamaIndex, or similar frameworks.

Understanding of RAG architecture, embeddings, and semantic search.

Familiarity with vector databases such as FAISS, Pinecone, or Chroma.

Knowledge of REST APIs and application integration.

Basic understanding of machine learning and deep learning concepts.

Familiarity with Git and cloud platforms such as AWS, Azure, or Google Cloud is an advantage.