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Overview
Engagement Type:6 Months contract(can be extended)
Location: Remote
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.
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.
