Azure GenAI Backend Engineer- India (RAG & Serverless Focus) (PL861)

Job description

Overview

Our client in the consulting space is seeking a Senior Consultant – Azure GenAI Backend Engineer to design and implement scalable Generative AI solutions with a strong focus on RAG architectures and Azure-native serverless platforms.

This role requires deep expertise in Azure AI services, backend system design, authentication mechanisms, and cloud-native architecture to deliver secure, production-grade AI systems.

Key Responsibilities

• Design and implement RAG-based architectures using Azure OpenAI and Azure AI Search.
• Develop backend APIs and services to support GenAI applications.
• Architect and deploy Azure serverless solutions (Azure Functions, Logic Apps, Container Apps).
• Build scalable data pipelines for indexing, embedding, and retrieval workflows.
• Implement CI/CD pipelines for AI systems using Azure DevOps or GitHub Actions.
• Define and implement system architecture ensuring performance, scalability, and high availability.
• Apply infrastructure as code using Terraform or Bicep.
• Collaborate with frontend, data, and AI teams to deliver end-to-end GenAI solutions.
• Enforce security, governance, and compliance best practices.

Job requirements

Required Skills & Experience

Core GenAI & Architecture

• Hands-on experience building RAG solutions in production.
• Strong understanding of LLMs, embeddings, vector search, prompt engineering.
• Experience with Azure AI Search and Azure OpenAI.
• Knowledge of agentic workflows (preferred).

Azure & Cloud

• Strong experience with:

• Azure Functions

• Azure Container Apps

• Azure App Services

• Azure Storage & Key Vault

• Solid understanding of Azure networking & identity management.

• Experience designing serverless architectures.

Backend Development

• Strong proficiency in Python, Java or node.js.
• REST API development and microservices.
• Strong system design capabilities.

DevOps

• CI/CD pipelines (Azure DevOps / GitHub Actions).
• Docker & Kubernetes (good to have).
• Infrastructure as Code (Terraform / Bicep).

Nice to Have

• AWS exposure.
• Consulting experience.
• Experience deploying AI systems in regulated environments.