Location: Toronto, ON, Canada
Contract Duration: 6 Month Contract
Work Type: Hybrid
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.
Full Stack Development
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Build and maintain modern web applications using React, Next.js, Angular, or similar frameworks
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Design and develop scalable backend APIs and AI orchestration services using advanced Python, FastAPI, Node.js, Java, or .NET
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Develop cloud-native and serverless applications using Azure services such as Azure Functions, API Management, Logic Apps, and Azure Service Bus
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Implement secure authentication and authorisation systems including OAuth2, OpenID Connect, JWT, and RBAC
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Apply software engineering best practices including testing, CI/CD, documentation, code reviews, and modular architecture
GenAI & RAG Engineering
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Design and implement AI-powered capabilities such as assistants, semantic search, summarisation, workflow automation, and intelligent retrieval systems
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Build and optimise enterprise-grade RAG architectures including ingestion pipelines, chunking strategies, embeddings, vector search, hybrid retrieval, reranking, grounding, and hallucination mitigation
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Integrate with LLM providers and orchestration frameworks including Azure OpenAI, OpenAI, Anthropic, Hugging Face, LangChain, Semantic Kernel, or LlamaIndex
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Develop prompt engineering strategies, tool/function calling workflows, guardrails, moderation pipelines, and output validation systems
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Implement observability and evaluation mechanisms for monitoring LLM quality, latency, and reliability
Data & Enterprise Integrations
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Integrate AI applications with enterprise systems such as SharePoint, Salesforce, ServiceNow, and internal APIs
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Develop data ingestion, enrichment, transformation, and retrieval pipelines
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Work with relational, NoSQL, and vector databases including PostgreSQL, Redis, Azure AI Search, Pinecone, Elasticsearch, or similar technologies
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Ensure strong governance, privacy, and security controls for enterprise and sensitive data
Performance, Security & Reliability
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Optimise LLM performance, scalability, latency, and operational cost through caching, batching, streaming, and token optimisation
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Design resilient distributed systems using retries, fallbacks, circuit breakers, and graceful degradation patterns
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Implement logging, monitoring, tracing, and observability solutions using OpenTelemetry, Application Insights, Grafana, or similar tooling
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Apply responsible AI principles including privacy controls, auditability, bias mitigation, and secure AI implementation practices
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Participate in system design discussions and contribute to scalable cloud architecture decisions
Required Skills & Experience
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3 to 8+ years of full stack software engineering experience
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Advanced Python programming and backend engineering capability
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Deep hands-on experience building production-grade RAG systems and LLM-enabled applications
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Strong experience with Azure-native architecture and serverless services
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Strong understanding of REST APIs, microservices, distributed systems, and cloud-native design
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Experience designing secure authentication and API security solutions using OAuth2, OpenID Connect, JWT, and RBAC
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Strong system design and scalable architecture capability
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Experience with CI/CD pipelines, testing frameworks, version control, and agile delivery methodologies
Preferred Qualifications
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Experience with Azure OpenAI, Azure AI Search, Azure Functions, Azure API Management, Azure Key Vault, and Azure Service Bus
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Familiarity with LangChain, Semantic Kernel, LlamaIndex, or similar AI orchestration frameworks
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Experience with vector databases, embedding models, reranking, and grounding techniques
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Experience with Docker, Kubernetes, Terraform, or Infrastructure as Code practices
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Understanding of enterprise security, compliance, and governance frameworks
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Experience designing event-driven and serverless AI systems on Azure
Tech Stack
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Frontend: React, Next.js, TypeScript, Tailwind
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Backend: Python (FastAPI), Node.js, .NET APIs
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AI Stack: Azure OpenAI, LangChain, Semantic Kernel, RAG Pipelines
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Data: PostgreSQL, Redis, Azure AI Search, Vector Databases
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Cloud & DevOps: Azure Functions, Azure API Management, GitHub Actions, Docker, Kubernetes, OpenTelemetry
