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 (Toronto)
🏢 Fortiv Technical
📍 Toronto