Top AWS Jobs in Toronto

Develop and maintain internal automation tools, applications, integrations, and infrastructure. Automate employee lifecycle processes, identity and access management, enterprise application workflows, and operational processes. Build solutions using software engineering and DevOps practices, troubleshoot systems, support deployments, and improve security, backup, disaster recovery, and reliability. Maintain documentation and collaborate with Engineering teams to deliver scalable internal platforms.

Design, develop, deploy, and optimize machine learning and deep learning models for customer-facing applications. Build data collection, preprocessing, training, and evaluation pipelines; conduct experiments; improve model performance and scalability; apply current AI research; and contribute to system design and code reviews.

As a Software Engineer, you will develop and maintain software systems, write clean code, and collaborate with team members, focusing on continuous learning and quality improvement.

Develop, test, and maintain marketplace applications using C#, .NET, Java, React and TypeScript. Write unit tests, participate in code reviews, debug production issues, support level-3 incidents, and collaborate with product and engineering teams. Continuously learn new tools (cloud, containers, AI) and follow engineering standards and automation best practices.

Support cloud infrastructure for machine learning training and deployment. Responsibilities include provisioning compute, orchestrating training jobs, improving MLOps workflows, assisting with model deployment and testing, investigating reliability, latency, and cost issues, collaborating with engineering teams, and documenting infrastructure tooling.

Own NerdWallet’s machine learning product roadmap from opportunity discovery through production and adoption. Partner with Data Science, ML Engineering, Data Engineering, Product Engineering, and business teams to define objectives, prioritize initiatives, manage technical dependencies, evaluate model performance, and measure business impact. Lead cross-functional delivery, influence senior stakeholders, establish ML product strategy, and mentor product managers and partners without formal authority.

Leads large-scale AI, digital, and cloud transformation programs from strategy through launch and realized business value. The role coordinates concurrent Agile teams, technical direction, architecture discussions, AI lifecycle governance, risk management, KPIs, stakeholder communication, staffing, and executive decision-making. It requires translating business goals into technical plans, managing complex dependencies and trade-offs, mentoring delivery leaders, and shaping delivery practices while partnering with clients on growth opportunities.

Design, build, and operate Chainguard’s container product, including registry features, image build pipelines, distribution systems, adoption tooling, and customer-facing services. Own projects end to end, make product trade-offs, collaborate with customers and product managers, improve observability and reliability, support on-call operations, and use AI coding agents safely. The role requires systems thinking across containers, registries, build infrastructure, and customer clusters in a remote-first environment.

Build secure, production-critical payment infrastructure for AI agent transactions. Design Python and FastAPI services, APIs, domain models, provisioning workflows, and cryptographic payment flows. Ensure scalability, reliability, observability, security, and comprehensive testing. Use Kubernetes, AWS, Terraform, and GitOps to automate infrastructure. Collaborate with product, security, infrastructure, platform, and card-network partners while applying AI-assisted engineering practices.

The Senior Forward Deployed Engineer manages client relationships, architecting, building, and closing deals while ensuring autonomous delivery of AI solutions. Responsibilities include discovery, architecture reviews, and capability transfer to clients, requiring significant engineering and client-facing experience.

Build and deploy production-grade AI applications, automations, and cloud-native systems within client environments. Own assigned workstreams, contribute to discovery and architecture sessions, translate ambiguous problems into deliverables, surface risks, and transfer durable solutions to clients. The role involves collaboration with engineers and business stakeholders, rapid prototyping, documentation, reusable practice artifacts, and potential progression toward independent account ownership.

Own and evolve the architecture of Agiloft’s AWS-based cloud services and CLM platform. Lead scalable, secure, resilient, and cost-efficient distributed-system design; define APIs, service boundaries, infrastructure, reliability standards, and technical roadmaps. Drive infrastructure as code, CI/CD, observability, automation, AI-assisted engineering, modernization, and developer experience improvements. Mentor engineers, influence cross-team technical direction, and partner with leadership and CloudOps on operational readiness, disaster recovery, and long-term platform strategy.

Supports the Trust & Assurance program by assessing vendors, responding to security questionnaires, collecting compliance evidence, tracking projects, maintaining policies and procedures, coordinating stakeholder requests, supporting security awareness training, and assisting with audits. The role requires familiarity with security and privacy frameworks, project coordination, documentation, Microsoft Office, and cross-functional communication in a cybersecurity SaaS environment.

Design, develop, test, deploy, and support scalable full-stack enterprise applications. Build React interfaces and Java, Spring Boot, Node.js, and Python back-end services, APIs, microservices, databases, and cloud deployments. Contribute to CI/CD, automated testing, security, troubleshooting, technical documentation, solution design, and cross-functional delivery. The role is hybrid, requiring two days per week in a downtown Toronto office.

Build and operate production AI platform services and customer-facing features primarily in Python. Develop agent runtimes, workflow orchestration, tool execution, evaluation, tracing, and observability systems. Integrate LLM workflows with distributed services and polyglot PHP and Java systems while optimizing reliability, latency, scalability, cost, and model quality. Provide technical leadership, architecture guidance, code reviews, mentorship, and cross-functional collaboration.

Architects cloud-scale solutions and technical strategies for strategic Canadian customers, aligning business objectives with secure, resilient, scalable platforms. Advises executives and technical stakeholders, leads cross-functional initiatives, develops prototypes and pilots, supports cloud and AI adoption, and addresses data residency, sovereignty, privacy, and security requirements. Builds trusted customer relationships, influences product improvements, presents technical thought leadership, contributes to communities, and mentors technical teams across Canada.

Design, build, and maintain full-stack web applications supporting AI-driven product discovery, search, recommendations, personalization, and consumer experiences. Lead frontend technical design, develop React and TypeScript components, integrate backend APIs and AI capabilities, optimize performance and reliability, and contribute to backend services. Mentor engineers, participate in technical reviews, and collaborate with product, design, data, AI, and engineering teams.

Build and operate scalable backend APIs, microservices, and integration services powering personalization, recommendations, search, and AI-driven consumer experiences. Own features from design through deployment, monitoring, and support. Collaborate with AI/ML and data engineering teams, improve service performance and reliability, contribute to system design and engineering standards, and mentor junior engineers.

Build and enhance full-stack, consumer-facing web applications for AI-driven product discovery, search, recommendations, and personalization. Develop reusable React components, integrate APIs and microservices, support backend services, and work with AI/ML and data teams. Contribute to scalable, secure, observable, and reliable solutions using search platforms, cloud services, databases, caching, and event-driven architectures. Collaborate cross-functionally with engineering, product, and design teams to deliver engaging digital experiences.

Build and enhance consumer-facing full-stack web applications for AI-driven product discovery, search, recommendations, and personalization. Develop React and TypeScript interfaces, integrate APIs and microservices, contribute to Java and Spring Boot backend services, and improve performance, accessibility, scalability, reliability, and observability. Collaborate with AI/ML, data, backend, product, and design teams while participating in design and code reviews and using AI-assisted engineering tools.

Lead applied AI initiatives from problem scoping and experimentation through production deployment. Build and fine-tune LLM, agentic AI, computer vision, NLP, and multimodal systems; establish MLOps workflows; evaluate model performance; and make trade-offs across accuracy, interpretability, latency, and cost. Partner with product and engineering teams, communicate technical decisions to stakeholders, and contribute to AI research rigor and technical direction.

Design and scale ML infrastructure, pipelines, deployment systems, and agentic AI workflows. Build cloud-based training and serving infrastructure, implement CI/CD, monitor model performance and drift, optimize costs, and ensure reproducibility and reliability. Collaborate with AI researchers and engineers to productionize models, automate retraining, manage data pipelines, and establish MLOps best practices.

Designs, builds, and operates scalable backend services, APIs, microservices, and event-driven systems supporting personalization, recommendations, search, and AI-driven consumer experiences. Partners with AI/ML, data, product, and engineering teams; integrates search and generative AI capabilities; improves performance, reliability, observability, security, and operational excellence. Leads technical delivery, mentors engineers, contributes to architecture reviews, and promotes engineering best practices.

Leads a backend engineering team responsible for designing, building, and operating reliable services, APIs, databases, and applications. Combines people management with hands-on technical leadership, architecture decisions, delivery planning, production support, incident response, and engineering process improvements. Partners across Product, Operations, QA, Design, and engineering teams while driving reliability, security, scalability, testing, CI/CD, and responsible adoption of AI-assisted development tools.

Lead platform engineering initiatives across cloud infrastructure, continuous delivery, developer environments, observability, and operational tooling. Own architectural decisions, build self-service infrastructure, improve CI/CD reliability and developer productivity, support AI-enabled engineering workflows, and participate in production on-call and incident response. This hands-on staff-level role also provides technical leadership and drives organization-wide engineering standards and reliability improvements.