Top AWS Jobs in Toronto

Lead AI engineering strategy and team to productize generative AI and autonomous systems. Drive end-to-end delivery from research to enterprise production, set architecture and reliability standards for LLMs, RAG, and multi-agent systems, and partner cross-functionally to measure business impact.

Develop scalable systems across product and infrastructure, lead architecture decisions, mentor engineers, and improve system performance using various technologies.

Lead end-to-end architecture and execution for key client products, mentor engineering teams in Next.js and cloud-native patterns, drive client-facing technical strategy and pre-sales scoping, own technical escalations and production SLAs, and write code when needed to ensure security, scalability, and delivery.

The Account Manager will build relationships, manage sales, operations, and marketing efforts for SOTI's enterprise customers, focusing on revenue growth and complex sales strategies.

The Data Engineer will provide technical support and deployment expertise, review data designs, and ensure code quality while collaborating with international teams on a strategic data initiative.

As a Staff Backend Engineer, you will architect and develop high-performance search and discovery systems, integrating AI capabilities and optimizing search functionalities while mentoring the engineering team.

Lead design and rewrites to build a scalable, resilient backend and cloud infrastructure. Own data storage performance, CI/CD pipelines, IaC, observability, incident response, and analytics. Mentor engineers, support customer onboarding and integrations, and drive reliability and scalability for AI-first products.

Lead end-to-end development and production deployment of agentic AI systems and full-stack products. Architect integrations with property management systems, improve reliability and observability, define engineering standards, and partner with product and operations to ship scalable AI agents across voice, text, and email.

Lead architecture and rewrites for a scalable, resilient AI-first platform. Own backend systems, data storage performance, deployment pipelines, cloud infrastructure via IaC, observability, incident response, and analytics. Mentor engineers and enable rapid customer onboarding and AI-driven automation.

Design, architect, and implement new features for the Spellbook application while collaborating with product management and improving existing functionalities using AI technologies.

The role requires designing and developing web applications and backend systems, integrating LLMs, and ensuring quality software delivery.

Lead the architecture and development of enterprise software tools, focusing on secure cloud systems, Node.js applications, and AI integrations while ensuring quality and reliability.

The Software Engineer will develop backend systems for an AI Energy Agent, including data integration, microservices, and scalable architecture, focusing on performance and reliability.

The Founding Software Engineer will build and enhance crisis-intervention software, focusing on mobile and web applications for government welfare, ensuring compliance and integrating data securely. Responsibilities include feature development, security enhancements, and managing interns.

As a Founding Engineer, you'll develop backend systems, optimize applications, and influence technical direction for an AI-powered knowledge platform.

The Full-Stack Engineer will develop and maintain web applications, translating UI/UX designs into responsive apps, optimizing applications, and writing clean code.

The Technical Architect is responsible for designing and implementing scalable solutions, engaging with customers and vendors, and mentoring engineering teams. They must contribute code and ensure successful delivery while maintaining a hands-on approach.

As a Data Engineering Architect, lead the transformation of data engineering into a modern platform, oversee architecture and technical leadership, design scalable systems, ensure data quality and governance, and mentor teams for a data-driven culture.

Lead architecture and evolution of Juniper Square’s distributed services, cloud infrastructure, and developer platform. Design scalable, multi-tenant, event-driven systems; build paved-road developer tooling, CI/CD, and observability. Hands-on coding, prototyping, and performance troubleshooting. Enable production AI/LLM workloads, ensure security/compliance, support enterprise upmarket needs, and mentor engineering teams to raise operational excellence and reliability.

The role involves designing and maintaining reinforcement learning environments, developing containerized execution environments, managing integrations, and collaborating with data operations for AI training.

The Senior Full Stack Developer will enhance Practice Better's AI-powered platform by developing software solutions, collaborating with teams, and optimizing performance.

Lead design and delivery of cloud-based data architectures and ETL/ELT pipelines for marketing analytics. Advise clients, integrate marketing data, build and optimize pipelines and reusable models, ensure data quality/security, mentor team members, and align technical solutions to business goals.

Lead design, build, and automation of AWS infrastructure using Terraform; create and improve CI/CD pipelines; support a multi-tenant SaaS platform for reliability and security; integrate AI tooling, AI agents, and MCP servers into DevOps workflows to boost delivery speed and reduce toil; establish monitoring, access control, and operational best practices; collaborate with cross-functional teams and communicate with stakeholders.

Design and implement core backend systems for a new AI-first SaaS platform using C#/.NET, Event Sourcing, CQRS, DDD and RabbitMQ; build cloud-native, containerized services on AWS with PostgreSQL; apply agentic AI tools to accelerate delivery; lead architecture, mentor engineers, and ensure reliable, well-tested production software.

Work directly with clients to scope, prototype, and deploy AI-powered workflows. Build scalable systems using LLMs, embedding and retrieval systems, and backend infrastructure. Translate messy data and operational constraints into production-ready solutions, iterate with stakeholders, and maintain deployed systems (including on-call support).