Top Tech Jobs & Startup Jobs in Toronto

Design, build, and deliver production-grade agentic AI applications and cloud-native systems. Own agent design, orchestration, multi-provider integrations, RAG, observability, and scalable microservices. Use AI coding assistants across the SDLC, run workshops and POCs, and partner with client stakeholders to move solutions into production.

Design, build, test, and maintain modern, production-grade web applications using AI-augmented workflows. Translate requirements into spec-driven artifacts, implement front-end (React/Angular/TypeScript) and back-end (Java or .NET/C#) code, develop APIs and integrations, deploy cloud-native solutions (containers, CI/CD) on major clouds (GCP preferred), and model AI-native development practices for teams and clients.

Lead cross-functional teams to architect, build, and deliver cloud-native, enterprise-grade web applications. Drive AI-native development practices, spec-driven workflows, and use of AI coding agents. Oversee frontend and backend architecture, APIs, integrations, CI/CD, containerization, and observability while mentoring teams and partnering with stakeholders to meet client objectives.

Lead design and delivery of enterprise-grade AI-native, agentic solutions end-to-end. Architect, build, and scale cloud-native systems (Kubernetes, microservices, serverless), implement multi-provider AI integrations, define evaluation metrics (accuracy, latency, safety, cost), and drive client workshops, POCs, and adoption of AI-native development practices while remaining hands-on throughout delivery.

Provide Level 3 support and optimization for IBM TWS/ZWS on z/OS, manage job schedules and calendars, monitor daily batch cycles, troubleshoot scheduling and batch-flow issues, perform upgrades and DASD management, support 24x7 production, and lead disaster recovery and compliance activities while mentoring junior staff.

Embed with clients to design, prototype, and deploy enterprise-ready agentic AI systems. Build retrieval, orchestration, policy routing, tool invocation, and observability; integrate multiple AI providers; use cloud-native patterns, CI/CD, and IaC; run workshops and measure agent accuracy, latency, safety, and cost.