Top AWS Jobs in Toronto

Build internal services and infrastructure that support privacy initiatives and consumer privacy rights. Collaborate with engineering, security, platform, and legal teams to integrate privacy technologies, define privacy architecture, mentor engineers, review implementations, and maintain reliable privacy solutions. The role focuses on responsible data use, governance, data flows, access controls, and reducing privacy risk across Block’s engineering ecosystem.

The Senior Full Stack Software Developer will design, develop and implement scalable applications, manage system architecture, mentor developers, and drive engineering excellence.

Own and improve automated test frameworks, CI/CD quality gates, and agentic quality tooling across engineering teams. Strengthen unit, integration, and end-to-end coverage; reduce flaky tests; build metrics, dashboards, and alerts; support release health decisions; and contribute to production features and internal tools. Partner with developers through test design, code review, and testability guidance while advancing AI-assisted engineering workflows.

Build and maintain Turso’s open-source database and cloud platform. Responsibilities include advancing PostgreSQL compatibility, ensuring software correctness through testing and verification, operating cloud APIs and data planes, and scaling systems to support substantially more databases. The role involves Rust development, embedded database technology, large-scale cloud operations, observability, reliability, performance improvements, and collaboration with customers, open-source communities, and distributed teams.

Own product experimentation and causal analysis for growth and engagement initiatives. Define KPI frameworks, evaluate retention and user behavior, build predictive models for lifecycle interventions, and assess segmented and long-term effects. Partner with Product, Engineering, and Design to recommend launches, iterations, or stops. Translate ambiguous questions into actionable insights, influence product priorities, and create reusable analyses using Python, SQL, Snowflake, dbt, Airflow, Spark, and AWS.

Design, build, and productionize LLM-powered systems (adaptive content, tutors, evaluators) and AI pipelines (RAG, embeddings, fine-tuning). Prototype and optimize models across modalities, define AI safety/evaluation/observability practices, and collaborate with product and engineering to measure learner outcomes and iterate.

Leads development of scalable cybersecurity products across frontend, backend, platform, and data infrastructure. Drives architecture, data pipelines, Kubernetes services, observability, API security, CI/CD, and cloud operations. Mentors engineers, leads cross-functional projects, aligns technical solutions with business goals, and evaluates responsible AI-assisted development practices. Requires extensive software engineering experience and expertise in React, TypeScript, Node.js, AWS, streaming systems, databases, distributed processing, and secure application design.

Manage company-wide risk assessments, risk registers, remediation tracking, control testing, audits, evidence collection, and SOC 2 and ISO 27001 compliance operations. Partner with engineering, security, IT, and legal to validate controls, automate evidence gathering, investigate failures, support customer and vendor security reviews, and improve continuous compliance practices in a fully remote environment.

Implements and configures an AI platform by integrating customer data, building ETL workflows, troubleshooting pipelines, managing recurring data deliveries, and automating manual processes. The role collaborates with customers and internal teams to define technical requirements, resolve implementation issues, deploy tailored solutions, and maintain data security and privacy.

Supports infrastructure reliability, Kubernetes, cloud deployments, CI/CD pipelines, monitoring, observability, and incident response. Builds automation with Python, Bash, or similar languages; manages Terraform, MySQL, Redis, and open-source technologies; and contributes to AWS infrastructure for AI and LLM initiatives. Collaborates with senior engineers and cross-functional teams, participates in on-call rotations, improves deployment processes, and applies security and problem-management practices.

Leads an engineering enablement team responsible for platform infrastructure, data, tooling, service standards, and customer implementation capabilities. Manages hiring, coaching, delivery, intake, prioritization, capacity planning, on-call operations, incident response, technical standards, and stakeholder relationships. Partners with Product, Security, Professional Services, and enterprise clients while driving reliability, automation, continuous improvement, and AI-assisted engineering practices.

Leads cloud security architecture, governance, and implementation across Azure, AWS, GCP, and hybrid infrastructure. Designs Zero Trust frameworks, secure landing zones, identity and network controls, threat models, and cloud security testing. Automates controls using Terraform, Python, PowerShell, and policy-as-code, integrates security into CI/CD, manages cloud security platforms, drives remediation and compliance, and mentors engineering teams.

Drive profitable growth across strategic enterprise accounts by executing account plans, generating pipeline, leading discovery, developing value-based proposals, managing stakeholder relationships, collaborating with technology partners, maintaining CRM accuracy, forecasting opportunities, and coordinating internal resources through closure.

Build and deploy production GenAI and LLM systems for enterprise clients in financial services and healthcare. Embed with customers to understand operational needs, design AI solutions, own implementation from strategy through deployment, develop evaluation suites, and improve reusable industry blueprints. Advise senior stakeholders, contribute to pre-sales, and help shape AI delivery tooling and frameworks using Claude, AWS, Python, and TypeScript.

Build and operate scalable database infrastructure for a multi-tenant SaaS data platform. Responsibilities include ingestion, query planning, distributed query execution, storage formats, APIs for AI-agent retrieval, tenant isolation, observability, and cloud-native deployment. The role requires architectural ownership, production software delivery, collaboration across product and infrastructure, and pragmatic decision-making in an early-stage, high-autonomy environment.

Monitor and analyze security events, lead incident response and employee investigations, conduct digital forensics, communicate findings to stakeholders, build investigation tools and automated workflows, and improve security operations processes. The role requires Python scripting, SIEM experience, cloud security knowledge across AWS and GCP, and forensic analysis across macOS, Windows, and Linux.

Serve as a senior technical advisor throughout pre-sales and post-launch engagements. Design integrations between Ada’s AI Agent and client systems, including CRMs, contact centers, commerce platforms, knowledge bases, and BI tools. Lead technical discovery, define requirements, scope bespoke solutions, build proofs of concept, and guide implementations to production with engineers. Support Customer Success, resolve technical obstacles, create reusable integration patterns, and provide product feedback based on recurring client needs.

Owns the lifecycle of international data products from business requirements through engineering delivery, release, and production monitoring. The role investigates data issues using SQL and Python, validates data quality, defines acceptance criteria, manages requirements and traceability, and translates findings into actionable business insights. It partners closely with stakeholders and data engineering teams to resolve incidents, improve data trust, and establish monitoring and stewardship practices.

Build and optimize data pipelines, applications, ETL processes, data models, warehouses, integrations, and reporting solutions for financial services clients. The role analyzes requirements, performs data profiling and source-to-target mapping, supports cloud and data migration initiatives, documents technical approaches, collaborates with client and project teams, contributes to internal accelerators, and mentors junior consultants.

Develop and deliver features for large-scale web applications using full-stack technologies across React, TypeScript, Node.js, Microsoft, and cloud stacks. Collaborate in agile Scrum or Kanban teams with architects, QA, product management, and stakeholders. Contribute to CI/CD, DevOps, troubleshooting, performance, security, and continuous improvement while building high-quality software solutions.

Own Burq’s platform engineering function across AWS infrastructure, Terraform, security, CI/CD, developer experience, observability, and reliability. Responsibilities include AWS account separation, Aurora migration, ECS and supporting service management, IAM hardening, SOC 2 readiness, GitHub Actions improvements, PR environments, Docker-based local development, monitoring, incident reviews, and platform roadmap execution.

Build and operate MITRE ATT&CK-aligned threat detections across cloud, endpoint, identity, email, and application telemetry. Own the detection lifecycle, advance detection-as-code with Git, testing, CI/CD, and observability, and improve data ingestion and enrichment. Evaluate AI-assisted security capabilities, translate threat intelligence into detections, and partner with Security Operations during investigations and incident exercises. The role requires strong Python and SQL skills, SIEM expertise, AWS and Linux knowledge, and independent project leadership.

As a Software Developer II, you will design and implement software systems, collaborate with peers, and improve product capabilities using various technologies, particularly TypeScript and Python.

Sets the technical vision for data science, leads complex credit risk, pricing, and loss forecasting models from conception through production, and architects scalable machine learning pipelines. Partners with senior stakeholders to shape strategy, establishes modeling and validation standards, mentors technical staff, and communicates recommendations to executives. The role requires deep fintech lending experience, advanced statistical and machine learning expertise, and hands-on production model deployment.

Design, deploy, and maintain HPC and MLOps infrastructure across cloud and on-prem clusters. Manage schedulers (Slurm/PBS), optimize MPI/CUDA stacks, storage and networking, automate deployments with Python/Bash, instrument systems, and enable AI training/inference pipelines while collaborating with product and support teams.