Senior Backend Developer, AI Systems
J
JobgetherAI recommendation systems
Remote working opportunities within Canada.Full-TimeSenior
SalaryCompetitive annual base salary ranging from CAD 143,000 to CAD 200,000. Potential additional bonus compensation, depending on the position and final offer.
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Job Details
- Experience
- At least 5 years of professional software engineering experience, with a strong focus on distributed systems.
- Required Skills
- AWSBackend DevelopmentPythonGCPKubernetesMicrosoft AzureSparkDistributed Systems
Requirements
- Have at least 5 years of professional software engineering experience, with a strong focus on distributed systems.
- Have experience designing, building, and operating large-scale, highly available, fault-tolerant distributed systems capable of handling high concurrency.
- Have strong software engineering fundamentals, including system design, unit testing, Git, code reviews, and continuous integration and delivery practices.
- Have strong Python programming skills and experience developing reliable, maintainable backend services.
- Have experience with distributed data processing frameworks such as Apache Spark, Apache Flink, or Apache Beam.
- Understand distributed systems fundamentals, including consensus, partitioning, replication, back-pressure, idempotency, and failure handling.
- Have practical experience with cloud infrastructure providers such as Google Cloud Platform, AWS, or Microsoft Azure.
- Be familiar with Kubernetes, Kafka, and gRPC.
- Have experience with automated testing, continuous integration, continuous delivery, and automated deployments.
- Be familiar with Docker and GitHub Actions.
- Demonstrate technical leadership, communication, collaboration, and mentoring skills.
Responsibilities
- Lead the architectural transformation of the core recommendation pipeline into a modern distributed system while maintaining production stability and reliability.
- Design, develop, deploy, and operate scalable, fault-tolerant backend services for personalization capabilities.
- Identify, investigate, and resolve performance bottlenecks, reliability issues, and scalability limitations across distributed systems.
- Collaborate with machine learning engineers and product stakeholders to translate requirements into architectural designs and production-ready solutions.
- Take end-to-end ownership of critical system components, including architecture, development, deployment, monitoring, and incident response.
- Promote engineering best practices in system design, code quality, automated testing, observability, and operational excellence.
- Improve distributed infrastructure to support high availability, fault tolerance, and efficient performance.
- Provide technical leadership and mentorship to engineers and contribute to architectural decisions.
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