Location: Toronto, ON, Canada
Contract Duration: 3 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
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
