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 evaluations

Monitor content safety, costs, latency, quality, and model performance

Build training, deployment, monitoring, and retraining MLOps pipelines

Prepare governed data with Delta Lake and Unity Catalog

Contribute reusable engineering patterns, standards, and best practices

Requirements

Five-plus years of software, data, or machine learning engineering

At least two years delivering production generative AI or LLM solutions

Proven LLMOps and MLOps experience, including model CI/CD

Databricks experience with MLflow, Unity Catalog, and Genie

Azure DevOps or GitHub Actions experience and solid SQL

Bring Databricks, MLflow, Python, SQL, and production LLMOps experience to governed enterprise AI delivery.

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