Paralucent

Remote (RAG & Serverless Focus) (PL861)

Toronto, CARemote OKContract$140k – $220k / year

About this role

Paralucent - Senior Azure GenAI Backend Engineer- Remote (RAG & Serverless Focus) (PL861) Skip to content Englishen Englishen Senior Azure GenAI Backend Engineer- Remote (RAG & Serverless Focus) (PL861) +5 more - Remote - - Delhi, Delhi, India - Mumbai, Mahārāshtra, India - Hyderabad, Andhra Pradesh, India - remote, Delhi, India - Bogota, Distrito Capital de Bogotá, Colombia - Toronto , Ontario, Canada - tech Job detailsApply Job description 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. 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

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