Senior Software Engineer, Backend & AI Platform
New
S
Sureel AIAI infrastructure
OntarioFull-TimeSenior
SalaryBase salary of $110,000 - $145,000 CAD. In addition to base salary, this role is eligible for a performance based annual bonus.
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Job Details
- Required Skills
- Node.jsTypeScriptRESTful APIsDevOpsMLOpsDistributed Systems
Requirements
- Have strong depth in backend and platform engineering, with the ability to work across the stack when needed.
- Have personally designed, built, deployed, and operated production systems.
- Be highly proficient in a modern backend language and comfortable working extensively with TypeScript/Node.js.
- Understand databases, networking, concurrency, APIs, distributed systems, queues, caching, cloud infrastructure, and failure modes.
- Be able to reason beneath frameworks and abstractions rather than treating them as black boxes.
- Have strong cloud, DevOps, and production infrastructure experience.
- Understand ML systems, including model serving, GPU workloads, inference, data pipelines, embeddings, evaluation, and MLOps.
- Use AI as part of the engineering workflow, including coding agents and AI-assisted development tools.
- Python experience is a bonus.
- Experience with GCP, Kubernetes, Docker, Terraform, DynamoDB or another NoSQL database, Meilisearch, vector search, distributed processing, GPU infrastructure, enterprise SaaS, developer APIs or SDKs, TypeScript/React, agentic development frameworks, or large-scale audio and media systems is a plus.
Responsibilities
- Design and build scalable backend services, APIs, data pipelines, and platform infrastructure.
- Own systems from architecture and implementation through deployment, observability, debugging, and iteration.
- Build infrastructure for high-volume AI, media, attribution, search, retrieval, and enterprise workloads.
- Work with the AI team to move experimental models and algorithms into reliable production services.
- Build MLOps capabilities for model deployment, inference, versioning, evaluation, monitoring, and reproducibility.
- Own and improve DevOps and cloud infrastructure, including CI/CD, infrastructure-as-code, containers, orchestration, security, and observability.
- Build enterprise capabilities such as authentication, RBAC, multi-tenancy, auditability, bulk operations, integrations, and configurable workflows.
- Make architecture decisions across databases, queues, caching, distributed systems, compute, storage, networking, reliability, and cost.
- Work with Product and, when needed, customers or AI partners to turn complex requirements into scalable technical solutions.
- Build internal tooling and automation for the engineering and research team.
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