Senior Software Engineer, Data Product
New
Remote-first flexibility across CanadaFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 8+ years
- Required Skills
- PostgreSQLPythonKafkaMLFlowFastAPIA/B testingMLOpsDistributed Systems
Requirements
- 8+ years of backend software engineering experience, with significant exposure to ML-powered systems in production
- Strong expertise in Python backend development, ideally with async frameworks (e.g., FastAPI)
- Solid understanding of PostgreSQL, distributed systems, and event-driven architectures (e.g., Kafka)
- Proven experience deploying and maintaining ML models as production APIs
- Hands-on experience with ML lifecycle tooling (e.g., MLflow or equivalent)
- Strong understanding of MLOps concepts such as model versioning, canary releases, shadow deployments, and A/B testing
- Ability to design observability frameworks for ML systems, including monitoring drift and prediction quality
- Experience leading technical design discussions and influencing architecture across teams
- Strong communication skills with the ability to explain technical and ML concepts to diverse audiences
- Collaborative mindset with strong partnership skills across Data Science and Engineering teams
Responsibilities
- Build and maintain backend services that deliver ML-based predictions and data-driven features through high-performance APIs
- Design scalable Python-based services supporting low-latency and high-throughput workloads
- Own the end-to-end lifecycle of ML-powered services, including deployment, monitoring, incident response, and continuous improvement
- Develop and maintain feature pipelines bridging offline model training with online inference systems
- Lead API design, system decomposition, and technical architecture reviews across data product surfaces
- Implement and improve MLOps practices including model versioning, rollout strategies, A/B testing, and rollback mechanisms
- Instrument systems for observability including latency, throughput, drift detection, and prediction quality monitoring
- Partner with Data Science to operationalize models and improve production performance
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