Job Description Our client is seeking a GenAI Full Stack Developer to design, build, and scale enterprise AI applications powered by Large Language Models, Retrieval-Augmented Generation (RAG), and Azure-native cloud services. This role is ideal for someone with strong backend engineering and system design capability who enjoys building production-grade AI systems across the full stack. You will work closely with product, UX, platform, and engineering teams to deliver secure, scalable, and reliable AI-powered applications with a strong focus on performance, maintainability, and responsible AI practices.

Requirements Full Stack Development Build and maintain modern web applications using React, Next.js, Angular, or similar frameworks Design and develop scalable backend APIs and AI orchestration services using advanced Python, FastAPI, Node.js, Java, or .NET Develop cloud-native and serverless applications using Azure services such as Azure Functions, API Management, Logic Apps, and Azure Service Bus Implement secure authentication and authorisation systems including OAuth2, OpenID Connect, JWT, and RBAC Apply software engineering best practices including testing, CI/CD, documentation, code reviews, and modular architecture GenAI & RAG Engineering Design and implement AI-powered capabilities such as assistants, semantic search, summarisation, workflow automation, and intelligent retrieval systems Build and optimise enterprise-grade RAG architectures including ingestion pipelines, chunking strategies, embeddings, vector search, hybrid retrieval, reranking, grounding, and hallucination mitigation Integrate with LLM providers and orchestration frameworks including Azure OpenAI, OpenAI, Anthropic, Hugging Face, LangChain, Semantic Kernel, or LlamaIndex Develop prompt engineering strategies, tool/function calling workflows, guardrails, moderation pipelines, and output validation systems Implement observability and evaluation mechanisms for monitoring LLM quality, latency, and reliability Data & Enterprise Integrations Integrate AI applications with enterprise systems such as SharePoint, Salesforce, ServiceNow, and internal APIs Develop data ingestion, enrichment, transformation, and retrieval pipelines Work with relational, NoSQL, and vector databases including PostgreSQL, Redis, Azure AI Search, Pinecone, Elasticsearch, or similar technologies Ensure strong governance, privacy, and security controls for enterprise and sensitive data Performance, Security & Reliability Optimise LLM performance, scalability, latency, and operational cost through caching, batching, streaming, and token optimisation Design resilient distributed systems using retries, fallbacks, circuit breakers, and graceful degradation patterns Implement logging, monitoring, tracing, and observability solutions using OpenTelemetry, Application Insights, Grafana, or similar tooling Apply responsible AI principles including privacy controls, auditability, bias mitigation, and secure AI implementation practices Participate in system design discussions and contribute to scalable cloud architecture decisions Required Skills & Experience 3 to 8+ years of full stack software engineering experience Advanced Python programming and backend engineering capability Deep hands-on experience building production-grade RAG systems and LLM-enabled applications Strong experience with Azure-native architecture and serverless services Strong understanding of REST APIs, microservices, distributed systems, and cloud-native design Experience designing secure authentication and API security solutions using OAuth2, OpenID Connect, JWT, and RBAC Strong system design and scalable architecture capability Experience with CI/CD pipelines, testing frameworks, version control, and agile delivery methodologies Preferred Qualifications Experience with Azure OpenAI, Azure AI Search, Azure Functions, Azure API Management, Azure Key Vault, and Azure Service Bus Familiarity with LangChain, Semantic Kernel, LlamaIndex, or similar AI orchestration frameworks Experience with vector databases, embedding models, reranking, and grounding techniques Experience with Docker, Kubernetes, Terraform, or Infrastructure as Code practices Understanding of enterprise security, compliance, and governance frameworks Experience designing event-driven and serverless AI systems on Azure

Tech Stack Frontend: React, Next.js, TypeScript, Tailwind Backend: Python (FastAPI), Node.js, .NET APIs AI Stack: Azure OpenAI, LangChain, Semantic Kernel, RAG Pipelines Data: PostgreSQL, Redis, Azure AI Search, Vector Databases Cloud & DevOps: Azure Functions, Azure API Management, GitHub Actions, Docker, Kubernetes, OpenTelemetry Hybrid: onsite 1 day a week