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.
- 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.
