GenAI Engineer (LLM) | Python | RAG | LangChain | AI Integration

ProLegion; Jaipur; On-site; INR 1,200,000 - 2,400,000; Full time

Job summary

ProLegion is seeking a GenAI Engineer (LLM Engineer) to build and scale LLM-powered SaaS applications with a secure, scalable architecture. You will integrate LLM APIs, design RAG pipelines, develop Prompt Engineering workflows, and manage Vector Databases to enable fast knowledge retrieval.

Work with Product and Engineering to deliver AI-powered features, monitor model quality, latency and cost, and ensure data security and compliance across the platform.

Qualifications

• 3+ years backend development with Python/Go/Node.js
• Experience building LLM applications using OpenAI, Claude, or Hugging Face
• Hands-on with LangChain or LlamaIndex
• Strong understanding of Retrieval-Augmented Generation (RAG)
• Experience with Vector Databases such as Pinecone, Weaviate, or pgvector
• Experience with Cloud Platforms (AWS, GCP, or Azure)
• Knowledge of APIs, Microservices, and Prompt Engineering

Responsibilities

• Integrate LLM APIs to build intelligent automation, parsing, and conversational AI solutions.
• Design and develop Retrieval-Augmented Generation (RAG) Pipelines.
• Create and optimize Prompt Engineering workflows for AI applications.
• Build, manage, and optimize Vector Databases for efficient knowledge retrieval.
• Ensure data security, compliance, and high-performance AI architecture.
• Monitor and optimize AI model quality, latency, and operational cost.
• Collaborate with Product and Engineering teams to deliver scalable AI-powered SaaS features.

Skills

Backend development

Python

Go

Node.js

LLM Applications

OpenAI

Claude

Hugging Face

LangChain

LlamaIndex

RAG

Vector Databases

Pinecone

Weaviate

pgvector

AWS

GCP

Azure

APIs

Microservices

Prompt Engineering

Security

Education

B.Tech / BCA or Technical Certification

Tools

LangChain

LlamaIndex

OpenAI API

Claude API

Hugging Face

Pinecone

Weaviate

pgvector

AWS

GCP

Azure

Job description

We are looking for a GenAI Engineer (LLM Engineer) to build and scale LLM-powered SaaS applications with a secure, scalable, and high-performance architecture. In this role, you will be responsible for integrating Large Language Models (LLMs) into production applications, developing intelligent AI workflows, and building solutions that leverage Retrieval-Augmented Generation (RAG), Prompt Engineering, and Vector Databases.

As an AI Integration Engineer, you will work on integrating LLM APIs, designing and optimizing RAG pipelines, managing Vector Databases, and collaborating with product and engineering teams to deliver scalable AI-powered features. You will also ensure application security, monitor performance, optimize latency and cost, and build reliable AI solutions that enhance the overall SaaS platform.

Key Responsibilities

• Integrate LLM APIs to build intelligent automation, parsing, and conversational AI solutions.
• Design and develop Retrieval-Augmented Generation (RAG) Pipelines.
• Create and optimize Prompt Engineering workflows for AI applications.
• Build, manage, and optimize Vector Databases for efficient knowledge retrieval.
• Ensure data security, compliance, and high-performance AI architecture.
• Monitor and optimize AI model quality, latency, and operational cost.
• Collaborate with Product and Engineering teams to deliver scalable AI-powered SaaS features.

Experience Required

• 3+ years of backend development experience using Python, Go, or Node.js.
• Experience building LLM Applications using OpenAI, Claude, or Hugging Face.
• Hands-on experience with LangChain or LlamaIndex.
• Strong understanding of Retrieval-Augmented Generation (RAG).
• Experience with Vector Databases such as Pinecone, Weaviate, or pgvector.
• Experience working with Cloud Platforms (AWS, GCP, or Azure).
• Knowledge of APIs, Microservices, and Prompt Engineering.

Preferred

• Experience in FinTech or SaaS domain.
• Exposure to LLMOps and Model Fine-tuning.

Skills Required

Must Have

• Model Integration.
• Retrieval-Augmented Generation (RAG) Pipelines.
• Vector Database implementation and management.
• Security and Compliance best practices.
• AI Performance Monitoring and Optimization.

Preferred

• CI/CD for AI Applications.
• Observability for AI systems.

Education & Qualification

• B.Tech / BCA or Technical Certification.