Top AWS Jobs in Toronto

Leads the Core Platform organization for Magna’s Unified Factory Platform, defining long-term architecture and technical strategy across distributed systems, IoT, APIs, data pipelines, cloud infrastructure, and SaaS scalability. Builds and manages engineering teams, drives reliability, security, operational excellence, deployment velocity, and performance metrics, and partners with Product, Hardware, Customer Success, and executive stakeholders. The role also oversees global compliance, platform commercialization, and multi-year roadmap execution.

Acts as a trusted technology advisor to enterprise clients, developing vertical-specific solution architectures, business cases, and sales strategies. Presents WWT’s OEM and services portfolio, drives opportunities and gross profit, coordinates internal and partner resources, and leads client engagements involving AI, cybersecurity, cloud, infrastructure, applications, and digital transformation. Builds executive relationships, supports proof-of-concept engagements, identifies client needs, and develops strategies to win large strategic accounts.

Build and operate production-grade AI systems using multimodal LLM APIs across text, image, and video. Responsibilities include developing backend microservices and REST APIs, deploying AWS/GCP infrastructure, implementing authentication, caching, telemetry, guardrails, resilience, testing, CI/CD, and operational documentation. The role owns AI feature lifecycles from prototype hardening through launch and ongoing support, including on-call incident response and cost and latency optimization.

Design, develop, and maintain scalable enterprise financial software and integrations. Build APIs, microservices, data pipelines, and event-driven solutions connecting ERP, payment, legacy, and third-party systems. Contribute to architecture, cloud adoption, CI/CD, DevOps, testing, documentation, and Agile delivery. Investigate production issues, perform root-cause analysis, improve reliability, and modernize enterprise technology across cloud and legacy platforms.

Leads architecture and delivery of complex, business-critical software systems. Modernizes legacy platforms, designs distributed services, APIs, event-driven architectures, and large-scale data-processing systems, while improving reliability, scalability, security, and observability. Sets technical direction across teams, establishes engineering standards, mentors senior engineers, and partners with product and architecture leadership to connect technical investments to business outcomes.

Build and maintain MLOps infrastructure for listener data and machine learning systems. Responsibilities include developing CI/CD pipelines, optimizing Apache Spark and Databricks workloads, productionizing models, managing scalable APIs and batch systems, implementing testing and monitoring, and supporting reproducible, cost-efficient cloud and containerized environments.

Designs and governs enterprise technology data architecture, including taxonomies, metadata standards, canonical data models, system-of-record strategies, lineage, and data quality frameworks. The role enables trusted data products, reporting, analytics, regulatory compliance, and executive decision-making across infrastructure, cybersecurity, cloud, AI, and technology management domains. It partners with architecture, engineering, risk, finance, governance, and data teams to establish scalable standards and drive adoption across a complex regulated organization.

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 design and delivery of scalable, governed data ingestion and integration pipelines for structured, semi-structured, and multimodal data to enable AI, ML, and Generative AI. Partner with product, data science, architecture, risk, and operations to define requirements, ensure data quality, lineage, security, and observability, and evaluate new technologies. Mentor engineers, drive cross-functional initiatives, improve platform resilience, and measure ingestion and AI data effectiveness.

Lead platform strategy and delivery, drive cloud modernization (AWS) and AI adoption, ensure platform performance, security, and scalability, manage cross-functional stakeholder relationships and vendors, oversee solution architecture and API/microservices design, and lead and develop a team while managing project financials and delivery across multiple workstreams.

Owns the enterprise observability strategy, roadmap, standards, and governance across infrastructure, platforms, applications, and digital services. Provides strategic direction to monitoring, APM, and SRE teams; aligns engineering and platform stakeholders; defines KPIs and maturity measures; presents outcomes to executives; optimizes tooling, vendors, cost, and scalability; and advances automation, self-healing, NoOps, security, compliance, and AI system explainability.

Own the vision, strategy, roadmap, and adoption of an enterprise agentic AI platform. Translate stakeholder needs into scalable product initiatives, prototypes, user stories, and backlogs while collaborating with engineering, data science, architecture, UX, and business teams. Define KPIs and demonstrate value through adoption, reliability, cost efficiency, user satisfaction, and ROI. Incorporate responsible AI, security, privacy, and compliance into product development.

Build and maintain full-stack web applications and backend services using React, TypeScript, Java, and Spring Boot. Develop accessible, responsive interfaces, reusable components, REST API integrations, and microservices. Collaborate with cross-functional teams, optimize performance, troubleshoot incidents, support security and testing practices, and contribute to cloud-native platforms, CI/CD, Agile delivery, and AI-powered conversational experiences.

Build, test, operate, and maintain scalable backend services, APIs, and microservices. Collaborate with product managers, architects, data engineers, and cross-functional teams to deliver secure cloud-native solutions. Develop integrations, work with databases, caching, messaging, and distributed systems, and troubleshoot production issues. Contribute to monitoring, observability, performance optimization, code reviews, CI/CD, automation, Agile delivery, and continuous engineering improvement.

Build and maintain full-stack web applications and backend services for PUMA’s global digital ecosystem. Develop React and TypeScript interfaces, Java and Spring Boot APIs, microservices, and AI-powered conversational experiences. Collaborate with product, design, architecture, data, and engineering teams; optimize performance, accessibility, security, observability, and reliability; troubleshoot production issues; contribute to technical design and engineering standards; and mentor junior engineers.

Design, build, and operate scalable backend systems, APIs, microservices, and distributed services. Lead technical implementations, improve performance, reliability, security, observability, and operational excellence, and collaborate with product, architecture, data, and engineering teams. Contribute to cloud-native and event-driven architectures, production support, incident management, technical reviews, automation, and continuous improvement. Mentor engineers through code reviews and technical guidance while delivering secure, high-performing business and consumer-facing applications.

Design, develop, and operate scalable backend APIs, microservices, and distributed systems. Build cloud-native, event-driven, data-intensive applications using databases, caching, messaging, search, and observability technologies. Lead technical design, improve performance and reliability, participate in architecture reviews, support production incidents, mentor engineers, and collaborate with product, architecture, data, and engineering teams.

Build and deliver enterprise integrations connecting AI solutions with tax and audit products, ERP systems, document platforms, and data warehouses. Develop document-processing, retrieval, embedding, and vector-search pipelines; enable AI agents through APIs and MCP; and establish data validation and quality controls. Implement secure authentication, testing, monitoring, deployment patterns, and operational runbooks. Partner with clients and cross-functional engineering, platform, consulting, and reliability teams to deliver reusable, production-ready solutions.

Build and deploy secure, production AI applications for tax and audit professionals, including RAG pipelines, custom agents, and agentic workflows. Develop scalable Python backend services, integrate with client and Thomson Reuters systems, and establish evaluation, observability, monitoring, and testing practices. Lead client delivery, translate ambiguous business needs into technical solutions, support deployments and incidents, and collaborate with AI engineers and senior technical leaders across multiple engagements.

Supports enterprise sales cycles for a data security platform by leading technical discovery, demonstrations, proof-of-concepts, and evaluations. Partners with account executives, security leaders, architects, Product, and Engineering to translate customer challenges into solutions, influence product development, build scalable sales processes, and enable internal teams and partners. Requires strong cybersecurity, cloud, SaaS, API, communication, troubleshooting, and technical presentation skills.

Build and operate production-grade LLM applications, agentic workflows, RAG and retrieval pipelines, AI memory architectures, and evaluation harnesses. Deploy and monitor AI services using cloud infrastructure, containers, APIs, and MLOps platforms. Configure AI gateways, manage model routing and costs, secure integrations, and optimize serving architectures. Collaborate with product, engineering, SRE, security, and software teams to deliver scalable AI capabilities.

Designs, builds, and maintains modern full-stack web applications, frontend features, backend services, APIs, and microservices. Leads technical delivery, architecture, performance, accessibility, security, observability, and cloud-native engineering initiatives. Collaborates with product, design, data, platform, and architecture teams; mentors engineers; supports production systems; and applies AI-assisted development and conversational commerce technologies.

Designs, engineers, and operates global DNS, DHCP, and IPAM services in a large enterprise environment. Responsibilities include DDI architecture, DNS and DHCP security, public and hybrid cloud DNS, IP and ASN management, CDN and traffic steering, network automation, troubleshooting, capacity planning, and merger or acquisition integration. The role also develops technical documentation, diagrams, scripts, and scalable solutions while collaborating with cross-functional teams and senior leadership.

Build and architect scalable backend systems using microservices, APIs, databases, and cloud services. Develop customer-focused product features, improve infrastructure and performance, resolve technical issues, and collaborate with product, commercial, and globally distributed engineering teams. The role also involves translating customer needs into product solutions and applying Agile development and CI/CD practices.

Lead backend engineering work for Affirm’s lakehouse infrastructure, delivering reliable, scalable distributed data systems. Own quarterly goals, guide engineers through ambiguous problems, collaborate with product and analytics stakeholders, improve technical standards, monitor system availability, support on-call operations, and mentor team members. The role focuses on large-scale data platforms and technologies including Iceberg, Flink, Airflow, Spark, Kafka, Snowflake, AWS, MySQL, Kubernetes, and Terraform.