Key Responsibilities Architect & Build: Develop, fine-tune, and optimize LLMs, multimodal models, and GenAI pipelines tailored for specific business use cases. Agentic Frameworks: Design and implement agentic workflows and multi-agent systems using frameworks like LangGraph, LangChain, or LlamaIndex. RAG & Vector Ops: Implement Retrieval-Augmented Generation (RAG) using vector databases, embeddings, and advanced prompt-engineering strategies. Data Engineering: Build scalable AI/ML systems and data pipelines using Azure Databricks, ADF, and PySpark. Deployment & MLOps: Deploy AI agents and models into production using Azure AI Foundry, ensuring adherence to enterprise best practices for security and scalability. Evaluation: Conduct benchmarking, A/B testing, and rigorous model evaluation to ensure performance and accuracy in production environments. Collaborate: Partner with product and domain teams to translate complex business problems into viable AI-powered solutions. Technical Skills & QualificationsCore Azure & Data Engineering (Primary) Azure Ecosystem: Extensive experience with Azure AI Foundry, Azure Data Factory (ADF), and Azure Databricks. Big Data: Strong proficiency in PySpark for data processing and pipeline management. Production Deployment: Proven track record of deploying AI agents on Azure with a focus on production-grade reliability and monitoring. Generative AI & Machine Learning GenAI Proficiency: At least 2 year of hands-on experience with LLMs (GPT, Llama, Claude, Mistral) and transformers. Agentic Workflows: Practical experience with LangGraph, LangChain, or LlamaIndex to build autonomous or semi-autonomous AI agents. Advanced Techniques: Experience in fine-tuning, prompt engineering, and working with multimodal AI (Vision + Language). Programming: Advanced Python scripting skills and familiarity with ML frameworks (PyTorch/TensorFlow). MLOps & Infrastructure Containerization: Familiarity with Docker and Kubernetes for model serving. Lifecycle Management: Knowledge of MLOps tools such as MLflow or Kubeflow to monitor and troubleshoot AI systems. Preferred Experience Direct experience solving high-impact business problems with AI solutions. Strong understanding of the end-to-end AI lifecycle, from data ingestion to real-time inference monitoring. Location: Toronto, ON (4 Days Onsite)