Top AWS Jobs in Toronto

The React Full Stack Developer will design, develop and maintain web applications, leveraging expertise in Java and JavaScript frameworks, specifically React, along with deep knowledge of cloud-native application development and microservices architectures.

Lead architecture and implementation of an Identity Security Fabric for AI agents. Build scalable backend services, integrate complex APIs, enforce auth and least-privilege for agent access, drive AI-native engineering practices, improve CI/CD/observability, and mentor engineers while partnering with product and design to deliver enterprise-grade identity governance and protection features.

Build and operate full-stack services for non-human identity governance across cloud and on-premises environments. Responsibilities include developing APIs, backend services, data pipelines, and occasional UI features; owning features through production; participating in design and code reviews; resolving reliability issues; applying secure development, testing, and observability practices; and mentoring engineers.

Lead and operationalize information security across systems, applications, and cloud. Manage SOC 2 and ISO 27001 audit cycles, vendor security assessments, incident response, security controls design, and compliance with privacy/regulatory requirements while partnering with technical and business stakeholders.

Design and build secure, scalable developer-facing security products and automation. Architect services, APIs, and SPAs; implement CI/CD with security gates; integrate SAST/DAST and cloud security tools; build analytics and AI-assisted vulnerability triage; drive adoption via demos and workshops.

Lead infrastructure strategy and architecture across AWS, platform engineering, deployment pipelines, and infrastructure as code. Build scalable cloud infrastructure, internal tools, automation, and backend services that improve developer productivity and operational efficiency. Partner with engineering and data teams, establish observability and operational best practices, mentor engineers, and make architectural tradeoffs balancing cost, performance, security, and development speed.

Build and deploy applied machine learning models and data-driven systems that classify, cluster, label, and enrich Internet-observed assets. Develop feature pipelines, training datasets, evaluation frameworks, confidence scoring, and production inference services. Collaborate with engineering, research, security, and product teams to transform raw Internet telemetry into high-quality datasets and customer-facing insights.

The Data Analyst will analyze large datasets, collaborate with stakeholders, develop data processes, and support data-driven decision-making to improve business efficiency.

Productionize, optimize, and scale LLM and ML inference across multi-cloud and Kubernetes. Deploy GPU-based workloads, implement routing/failover for model services, reduce latency via quantization/pruning/distillation, profile and tune hardware utilization, and build observability, monitoring, and cloud-native guardrails for enterprise AI services.

The Senior Data Engineer will scale the data ecosystem, construct data models, build systems for KPI tracking, and ensure data safety.

Design, deploy, and maintain AWS and Databricks data infrastructure, data lakes, and scalable ETL pipelines. Build custom ingestion services and connectors using Airbyte, Go, and Python; optimize performance, reliability, monitoring, and data quality. Implement cloud data governance, security, access controls, encryption, and compliance practices while collaborating with platform engineers, analysts, and stakeholders.

Build and ship end-to-end product features across frontend and backend: design APIs, develop backend services and asynchronous pipelines, implement responsive React UIs, optimize performance and reliability, work with AWS and queueing systems (e.g., SQS), and collaborate with product and design to deliver polished, scalable workflows.

Own and build end-to-end product features across frontend and backend: design APIs, develop backend services and asynchronous pipelines, implement high-performance React UIs, optimize reliability and scalability, work with AWS and queueing systems (e.g., SQS), and collaborate with product and design to ship data-intensive workflow automation.

Build and operate Valence's shared platform primitives (auth, routing, LLM gateway, deployment, observability, SLOs) to enable safe, scalable, and compliant delivery of AI-first products. Implement CI/CD gates, progressive delivery, rollback automation, security guardrails, region-aware routing, and developer-facing templates and tooling.

Design, build, and productionize LLM-based conversational and AI systems for enterprise coaching. Integrate structured and unstructured data, prototype end-to-end services, improve model behavior and observability, and collaborate with product and engineering to deploy scalable, reliable AI-driven coaching workflows.

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.

The AI Engineer will design and develop scalable AI solutions for finance, ensuring compliance and collaboration across teams. Responsibilities include coding, model lifecycle management, automation, and technical mentoring.

Join Zip's Core Infrastructure team to design, build, and operate global multi-region architectures. Own infrastructure components such as Kubernetes, AI infra, observability, deployment pipelines, and cost optimization. Collaborate with engineering teams to ensure scalable, reliable systems and support Zip's product roadmap.

The Lead Research Engineer will provide technical leadership, develop scalable ML solutions, and collaborate with teams to innovate and deliver products that enhance customer experiences in legal, accounting, and government sectors.

Lead design and build of high-scale, low-latency backend systems for premium ad formats. Define architecture for unified serving and rendering, enable experimentation and optimization, collaborate with Product/Design/Data Science, mentor engineers, and drive availability, scalability, and roadmap for ad formats.

Build highly available, scalable distributed systems and real-time services supporting Internet-wide scanning, data ingestion, APIs, security research, and machine learning initiatives. Responsibilities include developing data pipelines, messaging systems, databases, caching layers, tooling, and cloud or on-premises services; productionizing prototypes; collaborating with engineering, product, machine learning, and security teams; and maintaining reliable, observable systems.

Build and integrate LLM-powered applications and agents (RAG, vector DBs, agent orchestration) into the PureFacts SaaS platform, design evaluation and safety frameworks, implement APIs and scalable services, and partner with product and engineering to deliver AI copilots and automation that improve revenue analytics and operational efficiency.

Design, build, and maintain secure cloud, on-premises, and hybrid infrastructure. Develop and support Infrastructure-as-Code, automation, and deployment standards. Implement cloud security, identity, secrets management, monitoring, and telemetry. Integrate security tooling and automate controls, remediation, and operational workflows to improve threat detection, response, and operational efficiency.

Design and deliver cloud and AI security solutions, provide architecture and automation code, build proofs-of-concept, advise customers, publish reference architectures and technical content, and support training and evangelism for Fortinet cloud products.

The DevOps Engineer will design, build, and enhance cloud and automation platforms, collaborating with development teams to implement secure and scalable DevOps solutions.