Top AWS Jobs in Toronto

Leads a team of Technical Program Managers executing complex, multi-year technology initiatives, including cloud architecture updates, application convergence, integrations, and enterprise data migrations. Aligns Product, Engineering, Design, Data Science, and Risk stakeholders, resolves delivery bottlenecks, influences senior leaders, and establishes governance, metrics, release controls, and Agile operating practices. Coaches TPMs and improves delivery execution across a matrixed organization.

Lead technical strategy and pre-sales engagement for major financial services and insurance accounts. Guide enterprise customers through data and AI transformation, drive Databricks adoption, build technical champions, present solutions to executives, and coordinate sales and technical teams. Apply expertise in big data, data science, cloud platforms, machine learning, and production programming to deliver business outcomes across large global accounts.

Develop and maintain scalable, fault-tolerant software for Lyft’s Rider organization. Responsibilities include writing tested code, participating in code reviews and on-call support, debugging distributed systems, managing project deliverables, collaborating with product and engineering teams, improving processes, establishing architecture and roadmaps, and mentoring junior engineers.

Designs, builds, and operates secure, scalable cloud-native infrastructure for AI and generative AI workloads. Responsibilities include GPU and Kubernetes platforms, model serving, RAG infrastructure, APIs, microservices, networking, observability, CI/CD, infrastructure as code, security, compliance, and FinOps. The role defines AI cloud architecture and platform standards, leads solution design, operates systems using SRE practices, and mentors engineers. It requires extensive Azure or AWS experience, distributed cloud infrastructure expertise, and hands-on AI/ML platform engineering.

Develop and operate scalable cloud-based Privileged Access Management services and shared platforms. Design microservices, infrastructure, and multi-tenant systems using AWS, Kubernetes, Docker, Terraform, and distributed data technologies. Lead code and design reviews, write tests, troubleshoot performance issues, and improve service reliability. Collaborate with architects, QA, product, security, and operations teams while mentoring junior engineers and contributing to weekly releases.

Build and own production data pipelines, dbt models, semantic layers, and supporting infrastructure across Snowflake, BigQuery, Airflow, Fivetran, Databricks, AWS, and GCP. Partner with stakeholders across business functions to scope requirements, resolve data quality issues, monitor SLAs, support incident response, and apply data governance practices. The role requires autonomous delivery, strong communication, production-grade Python and SQL, and contributions to engineering standards, CI/CD, testing, and documentation.

The Engineering Manager leads Data Engineering teams, developing scalable data solutions and collaborating with stakeholders to drive McCain's digital transformation.

Supports the preparation, coordination, and submission of RFPs, RFIs, RFQs, and proposals. Responsibilities include reviewing requirements, coordinating stakeholder inputs, formatting and proofreading proposal materials, maintaining schedules and content libraries, conducting client and industry research, and preparing presales presentations. The role begins with a three-month trainee period and may transition to a full-time Presales Analyst position.

Designs, operates, and improves AWS and Azure cloud infrastructure supporting customer-facing higher education products. Owns Kubernetes platforms, infrastructure as code, deployment automation, CI/CD, monitoring, security, reliability, disaster recovery, and cost optimization. Troubleshoots complex infrastructure and application issues, leads incidents, participates in on-call rotations, mentors engineers, contributes to technical standards, and partners across Development and Information Security on modernization and production readiness.

Provides staff-level technical leadership for authentication, authorization, account lifecycle, and partner relationship systems. Designs APIs, data models, distributed services, migration strategies, and event-driven architectures. Leads cross-team initiatives, improves testing, observability, deployment safety, and reliability, and mentors engineers. The role requires strong backend and distributed-systems expertise, security-sensitive problem solving, AI-assisted engineering fluency, and experience with production operations.

Own GitHub administration and developer operations across multiple organizations, 900+ repositories, and 300+ developers. Establish security and operational baselines, automate configuration and compliance, manage integrations and access controls, and guide organization-wide adoption. Extend the model to Jira, Datadog, JFrog, and other SDLC tools while partnering with security and engineering teams. This remote-first role requires autonomy, consultative influence, change management, and responsible use of AI coding tools.

Provides remote and onsite IT support across client environments, managing tickets, troubleshooting Windows and workplace technology, administering Microsoft 365, Active Directory, endpoint management, networking, virtualization, and security tools. The role documents solutions, follows change-control and security standards, escalates complex issues, communicates with users and technical teams, and supports onsite infrastructure projects throughout the San Francisco Bay Area.

Designs end-to-end data solutions spanning ingestion, transformation, storage, modeling, and delivery for BI, analytics, AI/ML, and external consumers. Creates data flow diagrams and models, guides architectural and technology decisions, and collaborates with engineering, analytics, AI, governance, and business teams. Ensures solutions are scalable, secure, well-documented, performant, and compliant with financial-services governance and regulatory requirements using AWS, Snowflake, dbt, Alation, and Tableau Data Catalogs.

Design and build high-correctness Rust services on critical platform paths, introduce Rust for safety and performance, own systems end-to-end from architecture to operations, reason about concurrency and async, improve reliability and observability, collaborate across polyglot stacks, and mentor engineers.

Manage enterprise cloud platforms centered on Kubernetes and Docker, including deployments, production support, monitoring, disaster recovery, security compliance, networking, storage, and automation. Develop CI/CD and infrastructure-as-code solutions, maintain documentation and administrator accounts, troubleshoot incidents, coordinate with development and support teams, and research new technologies. The role requires enterprise IT, cloud, DevOps, observability, scripting, service mesh, and container-management experience in a hybrid Toronto-based environment.

Lead cloud security direction across AWS, GCP, and self-managed Kubernetes. Define standards, reference architectures, IAM controls, threat-modeling practices, detections, incident-response playbooks, vulnerability-management workflows, and secure infrastructure-as-code and CI/CD practices. Serve as a senior incident responder and cloud-forensics lead, secure distributed systems and data stores, drive cross-functional initiatives, and mentor security and platform engineers as a senior individual contributor.

Build full-stack features for an AI-powered insurance application using Python, React, Redux, PostgreSQL, and AWS. Integrate and deploy AI/ML models, develop data pipelines, design scalable cloud architectures, and create user experiences around intelligent functionality. Participate in architecture planning, code reviews, releases, cross-team collaboration, and engineering initiatives while researching new AI applications.

Develop full-stack features for an AI-powered insurance application using Python, React, Redux, PostgreSQL, and AWS. Integrate AI models and APIs for document processing, data extraction, and recommendations; build scalable backend services and intuitive user interfaces; and contribute to architecture, code reviews, releases, and cross-team engineering initiatives.

Design, build, and scale an agentic voice AI framework for banking customers. Develop LLM guardrails, grounding, policy enforcement, evaluation, tool-calling, and runtime architecture. Optimize real-time voice latency, maintain production reliability, reduce configuration friction, and collaborate with distributed product and engineering teams. The role also requires strong technical judgment, proactive ownership, AI-assisted development, and leadership through direction-setting, unblocking, and mentorship.

Define enterprise architecture strategies and transformation roadmaps across AI, data, cloud, and digital technologies. Design scalable AI/GenAI, data, and cloud architectures; establish standards and governance; advise C-suite stakeholders; lead modernization and transformation programs; support consulting proposals and account growth; and mentor architects and technical teams.

Leads the design and development of cloud-native platforms supporting generative AI agents. Responsibilities include building backend services, developer enablement tools, CI/CD pipelines, Kubernetes and multi-cloud infrastructure, platform security, observability, DORA metrics, and production engineering practices. The role requires mentoring, architectural leadership, cross-functional collaboration, and expertise in cloud-native systems, networking, infrastructure as code, and systems programming.

As a Staff Software Engineer on the Cash App Banking team, you will lead technical strategy, collaborate on decisions, manage large features, and mentor other engineers to enhance their skills in creating innovative banking solutions.

Lead the engineering organization for BlueCat’s Network Observability product line. Own technical strategy, architecture, roadmap, delivery, and operational excellence for data-intensive network monitoring platforms spanning SaaS and on-premise deployments. Build AI-driven observability capabilities, modernize legacy systems, establish engineering standards, and develop distributed teams. Partner with product, executive leadership, customers, sales, and support to achieve business goals and resolve strategic technical issues.

Own the definition and launch of a greenfield iOS and Android consumer app. Develop product concepts, write specifications, coordinate engineering delivery, define success metrics, and use data, experimentation, and user feedback to guide decisions. Apply computer vision and AI capabilities to create polished, intuitive consumer experiences while collaborating closely with engineering and cross-functional teams.

Conduct manual penetration testing, secure code reviews, and SAST triage across web applications, APIs, and AWS infrastructure. Develop AI-assisted security tooling, define testing approaches for LLM applications and agentic systems, and review AI-generated software before production. Communicate vulnerabilities, risks, and remediation priorities to engineering teams while improving secure SDLC practices.