Top AWS Jobs in Toronto

As a Senior Product Engineer, you will manage the full lifecycle of software features, engage with customers, and build scalable solutions using AI-assisted tools in a collaborative environment.

The Applied AI Engineer will own full-stack feature delivery, build AI pipelines, integrate financial APIs, and work cross-functionally on new product builds in an autonomous role.

The cybersecurity engineer will oversee operations bridging strategy and tech, handle incidents, automate patching, and develop policies.

Lead the Network Operations team in modernizing Marqeta's infrastructure from traditional data centers to a cloud-native architecture, ensuring 99.999% uptime. Oversee cloud networking, team management, automation initiatives, and corporate network infrastructure. Negotiate vendor contracts and ensure reliable connectivity across all locations.

Lead product strategy and execution for Auth0 platform infrastructure: scaling rate limiting, data streaming, cloud resilience, MCP enablement for AI agents, cross-team collaboration, and security/compliance to ensure high availability and performance for enterprise customers.

Lead the technical vision and evolution of Faire's Growth Platform, focusing on communication systems, targeting capabilities, and developer tooling while mentoring engineers and enhancing developer experience.

Lead DevOps and SRE initiatives for cloud-native data platforms: deploy and manage Kubernetes workloads (EKS/GKE), provision infrastructure with Terraform, maintain CI/CD (Jenkins, ArgoCD, GitHub Actions), automate with Python, monitor observability tools, optimize cloud costs, enforce security and SRE best practices, and collaborate with data teams to improve reliability and workflows.

Lead product strategy and execution for Redis Cloud across enterprise, digital-native, and AI customers. Define requirements, manage product lifecycle, work with customers and cross-functional teams, and drive launches, adoption, and technical decisions related to cloud infrastructure, data platforms, and AI workloads.

Lead design and implementation of test automation frameworks, architect AI-driven and self-healing testing ecosystems, build automation for web SaaS services, mentor SDETs, prototype agentic tooling, and collaborate across platform and product teams to improve reliability, scalability, and delivery velocity.

The Software Engineer will design and implement systems across the stack, focusing on AI-native engineering principles, distributed systems, and high-performance data processing.

As a Full Stack Engineer, you will develop frontend and backend features, write robust code, and collaborate with team members to meet project requirements.

Own and ship end-to-end full-stack features for a high-scale, AI-native publishing marketplace. Design APIs, optimize queries, deploy cloud-native apps, build LLM-powered capabilities, ensure security and privacy compliance, mentor engineers, influence architecture, and maintain reliability and performance under load.

Design and advance core infrastructure for engineering, ensure Kubernetes reliability, automate operations, and support AI infrastructure.

As a Forward Deployed Engineer, you will deploy and support software in secure client environments, troubleshoot complex issues, and ensure effective integration with existing systems. Responsibilities include installation, configuration, client engagement, and refining delivery practices.

As a Backend Engineer at Trust Wallet, you'll develop backend features for multiple platforms, maintain infrastructure, ensure system monitoring, and foster team growth.

The Sr. Software Engineer II will design, develop and maintain distributed systems, ensuring scalability, reliability, and performance while leading and mentoring team members.

Lead and architect scalable, high-performance Angular front-end applications. Collaborate with Product, Design, Backend, and QA in Agile teams. Write and review code, optimize UI performance, mentor junior engineers, and align implementations with technical strategy while integrating with backend services and modern CI/CD and cloud-native tooling.

Lead front-end development using Angular to design, build, and maintain scalable, resilient, and interactive web applications. Provide hands-on coding, code review, UI/UX best practices, collaborate with stakeholders and cross-asset UI teams, follow agile practices, and drive improvements and standards across front-end projects.

Lead development of secure, scalable full-stack banking applications. Design RESTful APIs and microservices with Java/Spring/Hibernate, build Angular front-ends, integrate SQL and NoSQL databases, enforce security/compliance, implement CI/CD, optimize performance, conduct unit testing and production troubleshooting, and collaborate with product, UX, and business stakeholders in an Agile environment.

Lead and grow a platform engineering team focused on Infrastructure, SRE, and DevOps. Set technical direction, deliver platform roadmaps, oversee cloud infrastructure and deployment pipelines, build AI-powered internal tools, improve developer experience, own incident management and on-call sustainability, and collaborate with product, security, and compliance to ensure secure, scalable, observable systems.

Build and maintain platform services for user lifecycle and HRIS integrations that process millions of records. Design, implement, and operate scalable, reliable microservices, APIs, and event-driven systems using cloud and container technologies. Contribute to design and code reviews, troubleshooting, migration from monoliths, estimation, and mentoring junior engineers.

Lead design, build, and maintenance of User Domain and HRIS integration services handling millions of user records. Architect scalable, secure microservices, mentor engineers, own features end-to-end, collaborate cross-functionally, and improve performance, reliability, and tooling.

As a Machine Learning Ops Engineer, you'll develop robust code, build and maintain MLOps infrastructure, QA systems, and mentor junior engineers to enhance AI model performance for mining clients.

As a Forward Deployed ML Engineer, you will apply machine learning to mining operations, develop neural network architectures, and communicate findings to non-technical stakeholders while working independently.

The Full Stack Engineer will develop and enhance a web-based dashboard platform, focusing on both frontend and backend systems, ensuring seamless data flow to AI models and clients. Responsibilities include creating user interfaces, RESTful APIs, secure data storage, and collaborating with teams on feature development.