Top AWS Jobs in Toronto

Supports pre-sales opportunities by translating customer requirements into technical solutions, leading demos and technical evaluations, designing permission schemas and proofs of concept, troubleshooting demonstrations, and collaborating with account executives, product, and engineering teams. The role requires backend programming, database, cloud, Kubernetes, API integration, and architecture expertise, plus strong customer communication. Travel is typically 3 to 4 days per month for customer visits and team offsites.

Build and operate Ground News’s data platform, including warehouse integrations, scalable dimensional models, analytical layers, and KPI infrastructure. Own ingestion pipelines, data quality, monitoring, incident response, infrastructure as code, and deployment workflows across AWS and GCP. Partner with analysts, ML engineers, and product stakeholders to create reliable, reusable data models and ensure accurate, observable, cost-effective data systems.

Design and build scalable data systems and pipelines, migrate legacy systems, ensure data governance, and collaborate with cross-functional teams to enhance data accessibility and reliability.

Own complex, cross-functional software programs from planning through release, coordinating engineering, product, design, QA, platform, and operations teams. Establish program governance, manage dependencies and risks, report to senior leadership, and improve PMO standards. Design reusable AI-powered workflows using tools such as Jira and Confluence, maintain prompt and automation libraries, mentor program managers, and serve as the PMO’s AI thought leader.

Leads architecture and development of scalable, high-performance full-stack applications using React, Next.js, TypeScript, and Node.js. Designs APIs, data models, authentication, and database integrations; ensures performance, accessibility, security, and maintainability. Collaborates with clients and cross-functional teams, mentors engineers, conducts code reviews, troubleshoots complex issues, and contributes to planning and technical documentation.

Lead Qumulo’s Northern California regional sales organization, owning go-to-market strategy, revenue targets, pipeline management, forecasting, strategic accounts, partner alliances, and executive customer engagement. Manage and develop Account Executives while collaborating with Sales Engineering, Marketing, Alliances, and Customer Success. The role focuses on enterprise sales of data storage, cloud, SaaS, and infrastructure solutions across technology, AI/ML, autonomous vehicle, and life sciences markets.

Leads partner technical discovery, designs and implements payment API integrations, builds scripts and lightweight services, and manages deployments from sandbox to production. The role develops AI-assisted engineering assets, delivery playbooks, and automation while collaborating with Sales, Product, and Operations. It requires customer-facing technical experience, software or solutions engineering expertise, and familiarity with APIs, integrations, cloud platforms, payments, and AI developer tools.

Lead design and delivery of cost-visibility and optimization tooling across cloud and AI infrastructure. Set technical strategy, build automation and monitoring, collaborate cross-functionally, ensure operational readiness, and mentor engineers to scale FinOps capabilities and self-service cost systems.

Own application-layer risk identification and remediation across consumer products and APIs. Build and operate AppSec tooling, Harden auth/authorization and GraphQL security, run HackerOne and offensive testing, lead threat modeling and security reviews, and build AI agents to automate triage and remediation while embedding secure-by-default practices into engineering workflows.

Design, operate, and improve cloud security for a multi-account AWS environment and Kubernetes clusters. Build detection, prevention, and automated remediation workflows, drive zero-trust access, shift-left security in pipelines, integrate cloud security tooling, mentor engineers, and partner with infrastructure teams on identity, networking, and secrets management.

Leads AI and Generative AI engagements from discovery through production, serving as the client and technical delivery lead. Combines project management with hands-on architecture and development of LLM applications, RAG pipelines, AI agents, APIs, and cloud-based services. Coordinates cross-functional teams, manages risks and delivery milestones, translates business needs into technical solutions, and oversees evaluation, monitoring, deployment, and production readiness.

Build and operate large-scale infrastructure and data pipeline platforms. Responsibilities include developing automation, monitoring system health, improving reliability and scalability, managing cloud infrastructure, participating in design and production readiness reviews, planning capacity, documenting processes, and responding to incidents through on-call rotations. The role requires production software development, infrastructure tooling, data streaming and storage expertise, and experience optimizing distributed systems in public cloud environments.

The Senior Site Reliability Engineer will design, optimize, and maintain MongoDB's multi-tenant distributed storage systems, ensuring reliability and operational safety while implementing automation solutions and participating in on-call support.

Leads architecture, development, deployment, and modernization of resilient Java and Spring Boot microservices supporting Markets Treasury and Money Markets. Designs high-throughput transaction systems, optimizes MongoDB and data pipelines, integrates trading platforms with accounting systems, and establishes testing, coding, and deployment standards. Provides technical leadership, mentoring, stakeholder collaboration, risk oversight, and performance tuning across cloud-native, event-driven applications.

Build and operate high-volume event streaming pipelines and analytical databases. Write production Elixir/Python services, integrate legacy pipelines, enforce event schemas and data quality, and provide monitoring, alerting, and SLA-driven observability for event data products.

Design, build, and operate a unified data platform: ingesting, processing, and exposing large volumes of data. Develop scalable ingestion pipelines, storage layers, observability, SLIs/SLOs, multi-tenant models, and metadata/ governance to enable secure cross-product data access and migrations from legacy systems.

Leads multi-team technical strategy and roadmap execution while remaining hands-on across frontend, backend, infrastructure, and data. Owns technical debt management, architecture consistency, API integrations, observability, SLAs, testing strategy, and developer tooling. Mentors engineers including senior staff, drives cross-team alignment, influences leadership, and delivers multiple significant initiatives in parallel. Requires deep Python and Django expertise, cloud and CI experience, and a computer science-related degree.

Design, build, and operate scalable backend automation systems using Go and PHP. Lead multi-sprint technical initiatives, architect resilient services, conduct code reviews, define SLOs, troubleshoot critical issues, mentor engineers, and collaborate with Product and Design. The role requires expertise in AWS, Kubernetes, distributed systems, databases, observability, and enterprise-scale software delivery.

Design, build, and support secure infrastructure across cloud, on-premises, and hybrid environments. Translate security and regulatory requirements into operational controls, improve monitoring/detection/response, support vulnerability remediation and audit readiness, evaluate security technologies, and automate hardening and standardization while partnering with IT, security, GRC, and business teams.

Staff Machine Learning Engineer sets technical direction for production ML architecture, deployment, serving, monitoring, testing, CI/CD, observability, and incident response. The role builds high-throughput, low-latency ML services; leads MLOps practices; supports recommendations, ranking, multimodal systems, LLMs, and AI agents; and drives cross-team technical decisions and multi-quarter initiatives.

Build and scale distributed backend services for growth infrastructure (notifications, referrals, targeting, lifecycle communications, ML ranking). Design resilient service-oriented systems on cloud, ship well-tested production code, run growth experiments/A-B tests, improve developer tooling and shared platform capabilities, and collaborate cross-functionally to deliver measurable acquisition and retention impact.

Design, build, test, and maintain enterprise integrations and automations across procurement, finance, and supply chain systems using MuleSoft and Zip iPaaS. Develop Python tools, write and optimize SQL, collaborate with stakeholders, ensure scalability and security, monitor production integrations, and contribute to documentation and DevOps practices.

Build and maintain full-stack software across frontend and backend systems. Develop APIs, backend services, integrations, and user-facing features; write tested code; debug production issues; and work with databases, AWS, microservices, and CI/CD. Collaborate with Product, Design, and engineering teams, contribute to code reviews, and improve reliability, performance, security, and user experience. Take ownership of work from development through production while continuously learning and using AI tools to improve engineering practices.

Build, evaluate, and deploy agentic AI systems that automate insurance operations. Own full-stack features and core product areas from design through production, shape architecture and tooling decisions, collaborate with customers and forward-deployed engineers, lead technical initiatives, establish engineering practices, mentor engineers, and contribute to product strategy.

Build and operate globally scalable, multi-region infrastructure for Zip. Own core infrastructure components such as Kubernetes, AI infrastructure, observability, deployment pipelines, and cost optimization. Lead system design and implementation, ensure operational excellence, and collaborate with engineering teams to support product growth. The role requires four years of software engineering experience, independent ownership of large-scale platforms, strong communication, and a relevant technical degree.