Top AWS Jobs in Toronto

Build and maintain large-scale, cloud-based detection and threat-intelligence systems. Design, implement, and ship high-throughput streaming data pipelines and production-grade APIs, mentor engineers, lead architecture and cross-functional initiatives, remove technical debt, and ensure operational excellence including on-call support and security best practices.

The role involves developing and maintaining web APIs and backend services using Java and Spring Boot, focusing on microservices and CI/CD in an agile environment.

Build and productionize ML systems for personalization, search, and recommendations. Apply statistical and ML models using Python and SQL, collaborate with stakeholders, and measure business impact across cloud environments.

Own QA for web and mobile releases: write and run manual and automated tests (web, mobile, API), build test frameworks, improve CI, track issues, mentor teammates, and influence requirements and quality processes.

Develop mobile-first, responsive Vue.js frontends focused on performance and UX. Build frontend management systems, participate in code reviews and team ceremonies, mentor junior engineers, and collaborate with backend teams.

Full‑stack software development for Tempo flagship products: implement frontend and backend features, write tests, review code, design APIs, optimize performance and scalability, work with product, design, QA, and docs teams, and operate in a remote, collaborative environment using cloud and Kubernetes.

Develop server-side applications using Kotlin and Java, collaborate with teams to enhance Atlassian plugins, and ensure code quality through testing and reviews.

Design and implement a governed AWS data foundation: reference architecture, layered (medallion) data models, governance (PII, classification, lineage, residency), reusable engineering patterns for ingestion/transform/serve, loose coupling to operational systems, and occasionally build data products hands-on to accelerate delivery.

Design, build, test, and deliver scalable machine-learning and NLP systems and large-scale data pipelines. Integrate ML models into production, write maintainable tested code, collaborate cross-functionally, and drive research-to-product development in an agile environment.

Design, build, and maintain core data pipelines and models for Lyft's Safety and Customer Care platform. Scale processing, tune Spark/SQL/MapReduce jobs, implement data quality systems, enable self-service ETL tooling, and collaborate with product, engineering, and analytics teams to deliver reliable reporting and insights.

Lead a team to design, build, and operate large-scale data pipelines and warehouses, ensure data quality and SLAs, collaborate cross-functionally, leverage AI/LLM, and drive key data initiatives from planning to delivery.

Lead a business analytics team to deliver data-driven insights, KPIs, dashboards and AI-enabled solutions. Partner with stakeholders to define strategy, automate reporting, prototype AI use cases, and build scalable analytics products while ensuring data quality and governance.

The Software Engineer will build and maintain internal developer tools, optimize CI/CD pipelines, and collaborate across teams to enhance developer experience and infrastructure reliability.

The Staff Software Engineer will develop and deploy high-performance machine learning systems, optimizing large language models for production environments. Responsibilities include ensuring high availability and low latency for AI applications and working collaboratively across teams.

The Site Reliability Engineer will develop, deploy, and operate AI infrastructure, focusing on high-performance and scalable machine learning systems using Kubernetes and cloud platforms.

The Technical Lead will design and implement backend systems, mentor engineers, optimize performance, and manage cloud architecture while ensuring scalable and reliable data processing.

Develop and deploy AI solutions for enterprise clients, focusing on large language models and agentic systems integration while ensuring quality, scalability, and security.

The Principal Advisor for Cloud Strategy is responsible for developing multi-cloud infrastructure strategies, hands-on engineering automation, and leading transformation initiatives while providing leadership and technical advice to executives and teams.

As a Senior Data Scientist, you will design AI solutions, lead NLP implementation, and mentor junior staff while transforming data into actionable insights.

Owner of cloud infrastructure and reliability for a cloud-native platform: design and maintain Azure/Terraform infrastructure, manage Kubernetes/Helm deployments, build CI/CD pipelines and observability, operate PostgreSQL at scale, lead incident response and support migration from legacy .NET/MSSQL to modern services.

Hands-on IT Systems Engineer responsible for endpoint management, identity and access administration, cloud and network support, infrastructure-as-code, compliance (SOC2/FedRAMP/CMMC), automation, and documentation. Own device lifecycle, IdP provisioning, VPN/segmentation, Terraform IaC, audit responses, and cross-functional IT initiatives to scale secure corporate operations.

The Senior MEAN Stack Developer will design, develop, and deploy scalable web applications, focusing on building robust REST APIs and microservices, while ensuring code quality and continuous improvement.

As a Machine Learning Ops Engineer, you'll develop robust code, build and maintain MLOps infrastructure, QA systems, and mentor junior engineers to enhance AI model performance for mining clients.

As a Forward Deployed ML Engineer, you will apply machine learning to mining operations, develop neural network architectures, and communicate findings to non-technical stakeholders while working independently.

The Full Stack Engineer will develop and enhance a web-based dashboard platform, focusing on both frontend and backend systems, ensuring seamless data flow to AI models and clients. Responsibilities include creating user interfaces, RESTful APIs, secure data storage, and collaborating with teams on feature development.