Top AWS Jobs in Toronto

Build backend infrastructure and APIs to integrate Lyft with emerging AI ecosystems and devices. Design scalable distributed systems, mentor engineers, drive high-impact projects, write and review maintainable code, participate in on-call rotation, and collaborate cross-functionally to deliver production-grade AI-enabled features.

Design, build, and optimize high-performance .NET Core backend microservices in a large distributed system. Implement observability (traces, logs, metrics), optimize DB access (PostgreSQL), improve service-to-service communication, support CI/CD, participate in architecture reviews, and troubleshoot production issues to ensure scalability, availability, and maintainability.

Lead development using AI-assisted and Agentic Development Life Cycle practices. Design and implement full-stack features, write automated tests, perform code reviews, ensure security and quality, monitor production, and mentor teammates to raise engineering standards.

Lead design and evolution of large-scale, distributed systems; drive AI-assisted development adoption; define technical vision; mentor engineers; improve observability, reliability, testing, security, and developer productivity across teams.

Define, validate, and govern an implementation-ready end-to-end platform architecture for Venn. Lead data, services, APIs, cloud, security, and integration design; produce ADRs, component designs, prototypes, sequencing roadmaps, and transfer knowledge to in-house teams for multi-country, multi-tenant, low-connectivity contexts.

Build and maintain core map data ingestion, conflation, and quality infrastructure to improve ETA accuracy and pickup precision. Lead features end-to-end, collaborate with cross-functional teams, write well-tested backend code, operate distributed systems, and participate in on-call rotations and knowledge sharing.

Maintain and improve reliability, scalability, and automation for user-facing production systems. Build infrastructure tooling, operate Kubernetes-based services, write IaC, participate in on-call and incident response, and advance observability and runbooks to reduce toil and improve platform reliability.

Provide first-line cloud and desktop support, manage OS images via Microsoft Endpoint Manager, handle patching and software distribution, monitor cloud infrastructure, troubleshoot user issues, assist senior IT staff, document work, participate in on-call rotations, and support cloud project deployments.

The Data Engineer is responsible for designing and optimizing data pipelines, ensuring data integration, and collaborating with teams on AI-driven applications.

Design, develop, and deploy scalable machine learning solutions, integrate ML with data warehouses, and optimize model performance in production environments.

The Sr. Machine Learning Engineer will design and deploy scalable ML systems, collaborate across teams, and ensure production readiness of ML models using modern frameworks and cloud services.

Senior individual contributor building and maintaining underwriting and credit decisioning ML systems for Cash App Borrow and Afterpay. Responsibilities include feature engineering, model training, calibration, experimentation, deployment, monitoring, and portfolio-level analysis. Collaborate with cross-functional teams to align models with business and regulatory goals and develop AI-native engineering workflows and governance for reliable, auditable model development.

Lead design, implementation, and support of secure, scalable AWS infrastructure. Drive on-premises to AWS migrations, automate provisioning with IaC (Terraform/CloudFormation), implement monitoring/logging, collaborate with DevOps/Security/Application teams, document architecture and procedures, and recommend emerging cloud solutions.

Lead large-scale, cross-functional engineering projects to deliver AI-native SaaS features. Own platform architecture for multi-tenant, cloud-native systems, drive engineering standards and developer tooling, mentor engineers, and operationalize LLMs, agent frameworks, and real-time data in production.

Lead design, implementation, and operation of a Databricks-based enterprise technology reference data platform. Oversee data ingestion, integration, transformation, pipelines, security, observability, and platform reliability. Drive standards, enable data products and reporting, partner with stakeholders, and build a high-performing data engineering and DataOps team to support analytics and AI use cases.

Lead implementation and enforcement of DevOps and SRE best practices for cloud-native data streaming, processing, analytics, and monitoring. Deploy and manage Kubernetes workloads (EKS/GKE), provision infrastructure with Terraform, maintain CI/CD pipelines, automate with Python, monitor performance, optimize cloud costs, and collaborate across teams to improve reliability and workflows.

Lead technical triage and resolution for full‑stack production issues, build internal tools and automations, expand observability, harden CI/CD and deployments, and partner with implementation and product teams to streamline customer rollouts and influence product roadmap around AI, cloud, and platform reliability.

Lead and implement platform architecture across Consumer Conversion, write and review production code, debug cross-system issues, drive release readiness and operational excellence, establish engineering standards and SLOs, coach engineers, and connect technical decisions to business outcomes.

Design and govern enterprise technology reference data architecture, taxonomies, and canonical data models. Enable metadata standards, lineage, systems-of-record strategy, data governance, and reusable data products to support reporting, analytics, and regulatory compliance. Partner with stakeholders across infrastructure, cybersecurity, cloud, AI, and business teams.

Lead and optimize enterprise Oracle databases for performance, availability, and security. Own backup/recovery and high‑availability strategies, troubleshoot complex incidents, collaborate with SRE/cloud/application teams, enable on‑prem to cloud migrations, and drive automation and monitoring improvements.

Build and maintain scalable integration and end-to-end test automation frameworks and CI pipelines. Optimize test execution, improve stability and reporting, support engineering teams with triage and best practices, write technical specs/ADRs, participate in on-call rotation, and collaborate across regions to evolve the companys test strategy and developer productivity tooling.

The Full Stack Engineer will design, build, and maintain scalable web applications, impact customer onboarding and integrations, write and review code, and support technical improvements.

Design, build, and deploy AI/ML solutions including data pipelines, model fine-tuning, and production deployment. Develop real-time model-serving microservices, implement monitoring for drift and performance, collaborate with data and DevOps teams, and prototype LLM and generative AI solutions using structured and unstructured data.

The Senior Forward Deployed Engineer will deliver technical solutions using AI, engage with customers, and lead project lifecycles while mentoring junior engineers.

The Linux Systems Specialist - Sr. will solve technical problems, manage client systems and enhance infrastructure while maintaining strong customer relationships.