Position: AI Engineer / Applied AI Scientist – GenAI & Azure Location: Hybrid- Toronto, ON Job type: Full-Time/Permanent Hiring

Overview Seeking a highly experienced Senior AI Engineer / Applied AI Scientist with deep expertise in Generative AI, LLMs, RAG architectures, and Azure AI ecosystems. The role focuses on designing and building scalable AI platforms, multi-agent systems, semantic search solutions, and enterprise-grade AI/ML pipelines. Key Responsibilities Design and develop end-to-end RAG pipelines including ingestion, embeddings, retrieval, and inference orchestration. Build LLM-powered applications and multi-agent AI systems using LangChain, LangGraph, and ReAct frameworks. Implement semantic search and vector indexing using Azure AI Search and Vector Databases. Develop scalable APIs and microservices using Python, FastAPI, or Flask. Build data pipelines using Azure Data Factory, Azure Functions, Event Hub, and Service Bus. Deploy and manage AI solutions using Azure OpenAI, Azure AI Studio, Azure ML, Databricks, and Fabric. Implement secure AI architectures with Azure AD, Managed Identity, Key Vault, and governance controls. Develop CI/CD pipelines, containerized deployments, and MLOps workflows using Docker, Kubernetes, Terraform, Azure DevOps, and GitHub Actions. Implement model evaluation frameworks using RAGAS, ROUGE, and AI quality metrics. Collaborate with engineering, data science, and business stakeholders to deliver enterprise AI solutions. Must-Have Skills 6+ years of experience in AI/ML Engineering or Backend Development. Strong expertise in Python, FastAPI, Flask, and AI/ML frameworks. Deep experience with LLMs, Generative AI, NLP, and RAG architectures. Hands-on experience with Azure OpenAI, Azure AI Studio, Azure Cognitive Search. Experience with LangChain, LangGraph, Agentic AI, and semantic search systems. Strong understanding of distributed systems, microservices, APIs, and event-driven architectures. Experience with SQL, NoSQL, and Vector Databases. Experience with Docker, Kubernetes, Terraform, CI/CD, and MLOps. Good-to-Have Skills Experience with GraphRAG, Knowledge Graphs, or autonomous AI agents. Exposure to SharePoint Graph API integrations. Experience in enterprise domains such as BFSI, Healthcare, or Insurance. Experience leading AI engineering initiatives or enterprise AI programs. Primary Skills Generative AI, LLM, RAG, LangChain, LangGraph, Azure OpenAI, Azure AI Search, Python, FastAPI, Vector Databases, AI/ML, MLOps, Kubernetes, Terraform, Azure AI Studio