Top AWS Jobs in Toronto

Design, build, and maintain production data infrastructure and pipelines; automate workflows; enable cross-team data access; perform large-scale data wrangling, statistical analysis, and visualization to deliver business insights.

Design, implement and maintain backend systems for a financial platform. Collaborate with quants, frontend and platform teams to build cloud-native AWS services, HTTP APIs and event-driven microservices using Rust and Python. Model complex financial trades, work with MySQL, MongoDB and other databases, and support performance-critical systems and observability.

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 role involves leading engineering teams in product development, driving innovation in software architecture, and integrating AI tools while providing mentorship and technical authority.

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.

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.

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.

Design and implement enterprise contact center transformations using Amazon Connect, generative AI (Bedrock/Claude), serverless backends, and full-stack React tooling. Build contact flows, AI agents, CDK infrastructure, observability, CI/CD and migration solutions; collaborate with customers, mentor engineers, and ensure production reliability, security, and accessibility.

Designs and develops enterprise cloud data solutions, including batch, near-real-time, and API-based pipelines. Builds data models, warehouses, lakes, and lakehouses while implementing governance, quality, lineage, and curation practices. Supports CI/CD, testing, monitoring, observability, security, and compliance. Collaborates with architects, product owners, analysts, and leadership on platform strategy and modernization. Mentors engineers, leads reviews, presents technical topics, and explores AI-enabled data capabilities.

Lead the architectural vision for cloud-based platform solutions, including scalable AWS infrastructure, integration flows, APIs, IAM governance, networking, observability, and CI/CD pipelines. Design secure, highly available platforms connecting RPA processes, enterprise applications, internal services, and downstream systems. Provide technical leadership, stakeholder management, architecture documentation, mentoring, and engineering best practices while supporting enterprise DevOps, platform engineering, and integration initiatives.

Design, build, and maintain scalable production pipelines and services to process clinical-grade 30x whole genome data. Optimize performance, reliability, and cost across AWS and hybrid environments. Develop tools and services (Python, C#, SQL, AWS) for genomic analysis, collaborate with cross-functional teams, ensure testing, observability, and validated output, and support consumer-facing DNA apps and reports.

Own architecture, development, and reliability of ecommerce web applications (Symfony storefront and Laravel back-office). Lead front-end JavaScript/CSS direction, optimize SQL schemas/queries, improve observability and incident response, reduce legacy technical debt, establish AI-assisted development workflows, mentor engineers, and drive end-to-end delivery of scalable customer-facing features.

Train and operate production machine learning models on Amazon SageMaker using AWS Trainium. Optimize PyTorch code for Trainium compilation, memory, throughput, and distributed training; diagnose hardware, compiler, device, data, and code issues; build cost-aware end-to-end training pipelines; and collaborate directly with client and internal engineering teams to deliver production workloads.

Lead the architecture, development, and delivery of scalable backend systems, APIs, data pipelines, and services supporting global 3D and computer vision applications. Drive technical direction, system performance, resiliency, observability, and maintainability. Mentor senior engineers, conduct architecture reviews, lead cross-functional initiatives, influence hiring and team growth, and shape engineering culture and long-term technical vision.

Lead design, implement, operate, and maintain AWS infrastructure using Infrastructure-as-Code (Terraform). Build local tooling and deployment/observability integrations (e.g., CircleCI), participate in on-call rotation, improve CI/CD performance, mentor engineers, manage incidents, and move platform toward Kubernetes while supporting Ruby/Python/Bash production workloads.

Lead design, development, and deployment of ML/AI solutions across the PureFacts platform. Own ML product vision and MLOps for multiple client models, build scalable data pipelines and cloud-based deployments, embed AI-driven automation and LLM features, ensure governance and explainability, and mentor a team of ML engineers to deliver measurable business impact.

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.

Operate, maintain, and improve Lyft's logging and metrics infrastructure. Build tooling to automate platform operations, define SLOs, monitor metrics, support incident response and on-call rotations, collaborate with engineering teams on observability, document processes, and automate repetitive tasks to improve reliability and scalability of distributed systems.

Lead end-to-end engineering for search and retrieval systems powering traditional search and agentic AI workflows: design ingestion/indexing, ranking, evaluation, productionize models, operate APIs on AWS, lead a squad, and collaborate with applied scientists to run experiments and measure real retrieval quality.

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.

Provide technical and business support for Citi applications, troubleshoot and resolve production incidents, participate in releases and disaster recovery testing, maintain runbooks and documentation, perform RCAs and change control, liaise with stakeholders and technology partners, and support stability and efficiency initiatives.