Lead LLMOps and MLOps engineering with Databricks, MLflow, Unity Catalog, and Azure across production AI solutions in financial services.This AI Engineer role focuses on operationalizing generative AI and machine learning use cases. You will establish evaluation pipelines, monitoring, versioning, guardrails, content safety, cost controls, and latency oversight. The work also includes preparing governed data with data engineering teams.Key Responsibilities:

Establish LLMOps practices for prompts, models, and evaluationsMonitor content safety, costs, latency, quality, and model performanceBuild training, deployment, monitoring, and retraining MLOps pipelinesPrepare governed data with Delta Lake and Unity CatalogContribute reusable engineering patterns, standards, and best practicesRequirements:

Five-plus years of software, data, or machine learning engineeringAt least two years delivering production generative AI or LLM solutionsProven LLMOps and MLOps experience, including model CI/CDDatabricks experience with MLflow, Unity Catalog, and GenieAzure DevOps or GitHub Actions experience and solid SQLBring Databricks, MLflow, Python, SQL, and production LLMOps experience to governed enterprise AI delivery.

📌 Databricks Llmops Engineer (Winnipeg)
🏢 Fortiv Technical
📍 Winnipeg