Senior Software Engineer – Data & ML Platform

New

JobgetherData and ML platform

Remote work opportunity in Canada.Full-TimeSenior

Salary not disclosed

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Job Details

Bachelor’s or Master’s degree in Computer Science, Software Engineering, Data Engineering, or a related field, or equivalent practical experience.
Strong programming skills in Python and SQL.
Strong understanding of APIs, backend service design, distributed systems, and production software engineering.
Experience building and operating production data pipelines end to end, including retries, idempotency, backfills, orchestration, and data-freshness monitoring.
Hands-on experience designing and operating production services in Azure or another major cloud platform.
Practical understanding of cloud infrastructure, including serverless and batch compute, object storage, identity and access management, monitoring, and resource management.
Experience with Infrastructure as Code tools such as Terraform, Bicep, or ARM.
Strong knowledge of Git, CI/CD, automated testing, and modern software development practices.
Experience working with ML or Operations Research codebases and model artifacts, including reading, running, packaging, and deploying models and research workflows.
Experience owning live production systems and taking over, understanding, and improving an existing codebase.
Experience with Delta Lake, Parquet, lakehouse architectures, DuckDB, Polars, or dbt is an advantage.
Familiarity with Azure Machine Learning, MLflow, DVC, Durable Functions, Airflow, Dagster, or Prefect is beneficial.

Own and improve backend services running in Azure, including serverless applications, batch workloads, and ML inference endpoints.
Manage deployments and production reliability, including CI/CD, monitoring, alerting, incident response, and operational runbooks.
Optimize cloud compute environments, including autoscaling, containers, identity, resources, and costs.
Design and build ETL/ELT pipelines that transform relational and document-based data into analysis-ready datasets.
Contribute to a lakehouse-style analytical layer and its supporting cloud infrastructure.
Build infrastructure as code and manage cloud services across multiple environments.
Develop internal services, APIs, and developer tooling as platform requirements evolve.
Build tooling and workflows for ML and Operations Research specialists to experiment, deploy, evaluate, and reproduce their work.
Convert research prototypes into production-ready services and workflows, including model packaging, versioning, deployment, and continuous integration.
Partner with Product, Data Science, Operations Research, and Engineering teams on architecture, reliability, performance, and technical solutions.

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