Software Engineer - Genetec AI Platform

Genetec is a global leader in video surveillance software with a diverse team across Canada, France, and Poland. The company has over 2000 employees worldwide and focuses on innovative strategies and software technologies to enhance security environments.

• Hands-on experience with MLFlow, KServe, and AirFlow.
• Knowledge of labelling/annotation tooling.
• Experience with experiment tracking tools.
• Familiarity with model runtimes and deployment using Triton Inference Server and OpenVino Model Server.
• Knowledge of DevOps, CI/CD for ML, GitOps workflows, and automated deployment strategies.
• Proficiency in telemetry, data drift, and quality monitoring.
• Strong skills in Kubernetes and Terraform for infrastructure automation.
• Experience with scaling services in cloud environments.
• Solid coding skills in at least one language: Golang or .NET, with motivation to learn both.

Nice to have:

• Expertise in data platforms, including batch and streaming pipelines with knowledge of Databricks, Spark, and/or Delta Lake.
• Knowledge in labelling/annotation tools and experiment tracking tools.
• Strong understanding of data quality, data versioning, and lineage tracking.
• Knowledge of the Champion/Contender deployment model.
• Understanding of responsible AI governance and compliance.

• Build and maintain self-service data acquisition, annotation, transformation, and versioning capabilities for developers and data practitioners.
• Facilitate data lineage tracking and data governance.
• Provide tooling to build transformation pipelines with integrated data quality checks.
• Enable experimentation with data and models, allowing researchers to track and reproduce experiments.
• Provide access to compute clusters for running model training pipelines and experiments.
• Facilitate full traceability of data, code, and artifacts across experiments and data transformation pipelines.
• Design and operate secure, automated deployment workflows for moving models from experimentation to production.
• Implement continuous deployment pipelines with versioning, parallel deployments, and rollback capabilities.
• Optimize runtime environments to meet latency and performance targets, supporting multi-cluster and multi-tenant scaling.