Senior Backend Engineer (SaaS, Data & AI)

TL;DR

Senior Backend Engineer (SaaS, Data & AI): Designing and delivering SaaS backend systems powered by data, machine learning, and AI with an accent on multi-tenant architecture, scalable APIs, and production-grade reliability. Focus on integrating ML/AI into real customer workflows, improving performance and observability, and leading technical direction across product areas.

Location: Toronto, Ontario

Company

hirify.global builds AI-powered IT management and cybersecurity software for Managed Service Providers (MSPs) and internal IT organizations worldwide.

What you will do

• Own backend architecture and technical direction for a multi-tenant SaaS platform.
• Design and build APIs and services using REST, GraphQL, or gRPC.
• Deliver backend features end-to-end, including data modeling, business logic, and system integration.
• Integrate data, ML, and AI capabilities into product workflows such as automation, recommendations, and insights.
• Improve system performance, scalability, and reliability across critical services.
• Implement monitoring, logging, and alerting; lead design reviews and mentor engineers.

Requirements

• 10+ years of software engineering experience in a production environment.
• Experience designing and building backend systems for SaaS applications.
• Experience delivering complex, user-facing features end-to-end with Product teams.
• Experience building APIs and services (REST, GraphQL, or gRPC).
• Experience working with cloud platforms (AWS, GCP, or Azure).
• Experience integrating data, ML, or AI capabilities into production systems.

Nice to have

• Experience with backend languages such as Node.js, Java, Go, or similar.
• Experience with Python and data tooling (e.g., pandas, ML frameworks).
• Familiarity with data modeling, relational databases, and caching strategies.
• Understanding of ML/AI system integration and common failure modes.
• Experience with containers and distributed systems.

Culture & Benefits

• High-growth environment focused on innovation, accountability, and results.
• Hands-on role partnering with Product, Design, and Data/ML teams.
• Emphasis on engineering standards, reliability, and operational visibility.
• Mentorship and design review leadership to raise team quality.