GenAI Full Stack Developer- India (PL854)

Location: India Contract Duration: 12 Month Contract Work Type: Remote 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, , Angular, or similar frameworks • Design and develop scalable backend APIs and AI orchestration services using advanced Python, FastAPI, , 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, , TypeScript, Tailwind • Backend: Python (FastAPI), , .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 Originally posted on Himalayas