Job Description

Location: India

Contract Duration: 3 Month Contract

Work Type: Remote

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