Senior Software Engineer, Backend & AI Platform - Sureel AI

Role overview

A senior backend and platform engineering role focused on building the technical backbone for an AI rights, licensing, attribution, and protection platform serving creators, rightsholders, AI companies, and digital media platforms. The position is highly hands-on, spanning backend, platform, DevOps, and MLOps work to turn advanced research into reliable enterprise products operating at media scale.

Responsibilities

- Design and build scalable backend services, APIs, data pipelines, and platform infrastructure handling high-volume AI, media, attribution, search, retrieval, and enterprise workloads - Own systems end-to-end, from architecture and implementation through deployment, observability, debugging, and iteration - Build and improve MLOps capabilities spanning model deployment, inference, versioning, evaluation, monitoring, and reproducibility, partnering with the AI team to productionize experimental models - Develop enterprise capabilities such as authentication, RBAC, multi-tenancy, auditability, bulk operations, integrations, and configurable workflows - Own and improve DevOps and cloud infrastructure including CI/CD, infrastructure-as-code, containers, orchestration, security, observability, and deployment reliability - Make architecture decisions across databases, queues, caching, distributed systems, compute, storage, networking, reliability, and cost - Create internal tooling and automation that helps engineering and research teams move faster

Requirements

- Senior-level depth in backend and platform engineering with the ability to work across the stack when needed - A track record of personally designing, building, deploying, and operating meaningful production systems - High proficiency in a modern backend language with extensive TypeScript/Node.js experience; Python experience is a plus - Strong grasp of databases, networking, concurrency, APIs, distributed systems, queues, caching, cloud infrastructure, and failure modes - Solid cloud, DevOps, and production infrastructure experience, with the ability to reason beneath frameworks rather than treating them as black boxes - Familiarity with ML systems including model serving, GPU workloads, inference, data pipelines, embeddings, evaluation, and MLOps - Comfort operating in a fast-moving startup environment with significant ownership and ambiguity, using AI to extend engineering judgment

Nice to have

Experience with GCP, Kubernetes, Docker, Terraform, AWS DynamoDB or other NoSQL databases, 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.

Benefits and work setup

Base salary of $110,000 – $145,000 CAD plus a performance-based annual bonus.