Our utilities client is seeking a Senior Data Architect to assist with enterprise AI initiatives (AI & GenAI) and to design and implement contemporary scalable data platforms (Azure) and AI innovation using LLM, RAG, and MLOps/LLMOps frameworks Candidates must be located in Calgary or Edmonton.

Hybrid work model of in-office and WFH Must Haves: 8+ years of experience as a Data Architect or Solution Architect with a focus on MS Azure enterprise data architecture focusing on scalable platforms , hybrid lakehouses , and analytical databases Experience with AI & GenAI architecture including LLM, RAG , model fine-tuning, and NLP Experience with MLOps/LLMOps frameworks supporting end-to-end production deployment and operationalization Experience in ETL/ELT, CDC pipelines , batch processing, real-time streaming, and event-driven API architectures Master’s degree in Computer Science, AI, Machine Learning, Data Science, Software Engineering, Statistics, or Applied Mathematics Strong Nice to Haves: PhD in Computer Science, AI,



Machine Learning, Data Science, Software Engineering, Statistics, or Applied Mathematics Strong communication skills with Stakeholder management experience Capable of identifying and operationalizing opportunities Responsibilities Design and implement scalable enterprise data architectures and hybrid lakehouse platforms Architect and operationalize enterprise AI and GenAI initiatives Drive lifecycle management, cost optimization, capacity planning, and resource utilization Deliver batch, streaming, event-driven API, and CDC data pipelines using ETL/ELT frameworks Develop predictive modeling, forecasting, anomaly detection, recommendation, and optimization solutions Architect executive-ready dashboards, operational reporting, and intelligent summarization systems Champion programs from requirements discovery through architecture and design

📌 Senior Data Architect to assist with enterprise AI initiatives (AI & GenAI) and to design and implement modern scalable data platforms (Azure) and AI innovation using LLM, RAG, and MLOps/LLMOps frameworks
🏢 S.i. Systems
📍 Edmonton