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Senior Solutions Architect

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Mississauga, Canada Global ERP Solutions Full time

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 & Qualifications: Core 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 1 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 (Hybrid – 3 Days Onsite) #J-18808-Ljbffr