Top Tech Jobs & Startup Jobs in Toronto

Manages a network reliability and resiliency team, including career development, prioritization, performance reviews, crisis response, and onboarding. Drives automation, infrastructure reliability initiatives, process improvement, monitoring solutions, cloud operations, and adoption of new technologies. Provides training and support to partner teams while promoting scalable, geographically distributed service operations and continuous improvement.

Own production database troubleshooting, performance analysis, incident root-cause analysis, and reliability improvements across large-scale PostgreSQL and MariaDB fleets. Develop observability and automation, influence multi-tenant cloud infrastructure decisions, support distributed web applications, and mentor engineers. The role requires database performance tuning, Unix administration, distributed systems expertise, stress testing, and experience with SaaS, PaaS, or cloud infrastructure products.

Owns production database troubleshooting, performance analysis, incident root-cause analysis, observability, and automation across large-scale multi-tenant PostgreSQL and MariaDB environments. The role supports distributed web applications on Unix and cloud infrastructure, influences platform decisions, develops mission-critical software, and mentors engineers. It requires database performance tuning, stress testing, distributed systems expertise, scripting, and familiarity with DevOps tools such as Kubernetes and CI/CD pipelines.

Design, build, deploy, and operate production optimization applications using advanced algorithms, machine learning, APIs, data pipelines, concurrency, and automated evaluation. Specify and review AI coding-agent work, own system quality and reliability, monitor production services, respond to issues, and collaborate with customers and cross-functional teams to translate business requirements into scalable solutions.

Leads cybersecurity sales across Canada by developing go-to-market strategy, driving enterprise revenue, recruiting and coaching sales executives, managing territories and quotas, maintaining forecasts, and closing C-level opportunities. The role partners with ServiceNow teams, solution consultants, marketing, professional services, and channel partners to accelerate pipeline, expand customers, and establish the integrated security platform as a market standard.

Generate new SaaS business revenue through account and territory planning, prospect research, field sales, and deal negotiation. Build C-suite relationships, lead account strategies, coordinate virtual teams and specialists, advise customers on IT roadmaps, achieve sales targets, and promote customer success. The role requires integrating AI into business processes and willingness to travel up to 50%.

Manage a systems engineering team designing, deploying and scaling multi-cloud infrastructure (private and public) including a Canadian sovereign GCP cloud. Provide technical leadership, roadmap planning, hiring and development, cross-functional collaboration, architecture reviews, risk mitigation, and operational excellence for global production services.

Lead cloud security engagements for financial services in Canada, supporting pre- and post-sales, questionnaires, incident investigations, threat analysis, and AI/GenAI risk discussions. Drive customer meetings, presentations, cross-functional remediation, design improvements, and contribute to security messaging, contract negotiations, and industry standards participation.

Design, build, and manage Canadian sovereign and global cloud infrastructure using code. Automate provisioning (Terraform, Crossplane), develop services in Go/Python, onboard services, operate Kubernetes, use Puppet/Ansible, and collaborate with SRE, networking, and system administration to ensure secure, scalable environments.

Own design and implementation of the agent execution harness and orchestration runtime for enterprise-scale agentic AI. Ensure reliability, observability, prompt/versioning infrastructure, LLM integration and model routing, evaluation frameworks, and technical leadership to drive production-quality, cost-efficient AI workflows.