Senior Consultant – Azure GenAI Backend Engineer (RAG & Serverless Focus) (PL795
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Job Description
Duration: 6-12 months contract (with the possibility of extension)
Work Model: Remote, Canada
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.
Requirements
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.
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