Top AWS Jobs in Toronto

Lead architecture and hands-on development for a cloud-native data enrichment service. Design scalable, reliable, and high-performing solutions; establish engineering patterns, tooling, and infrastructure practices; and guide modernization efforts. Collaborate with engineering, product, and UX stakeholders, mentor senior developers, communicate technical tradeoffs, and drive adoption of effective practices across teams.

Build and ship end-to-end features across frontend and backend using React, TypeScript, Node.js, NestJS, GraphQL, PostgreSQL, and AWS. Design scalable APIs, data models, and services; use AI coding agents; ensure automated testing and code quality; troubleshoot production systems; and collaborate with Product, Design, QA, and DevOps. Contribute to secure engineering practices and AI-assisted development workflows.

Build and scale Euler’s AI-native PRM platform across the front end and back end. Develop React and Next.js applications, Python and Node.js services, CRM and partner-tool integrations, AWS and Cloudflare microservices, REST APIs, and deployment workflows. Collaborate with engineering and AI teams, apply secure coding practices, maintain CI/CD pipelines, and ship scalable features while working US East Coast hours.

Lead full-stack platform architecture and development across React, Node.js, and PHP. Improve scalability, performance, APIs, databases, CI/CD, cloud infrastructure, testing, SEO, and payment workflows for an eCommerce platform. Own the design system, conduct final code reviews, establish engineering standards, mentor engineers, and contribute hands-on to complex software development.

Develop and maintain Java backend applications using JSP and J2EE. Deploy scalable serverless and microservices-based services on AWS, manage CI/CD pipelines, conduct architectural reviews, optimize performance, and enforce code quality. Collaborate with clients, product teams, and cross-functional stakeholders to gather requirements and deliver solutions. Applicants must have at least six years of Java experience, AWS expertise, strong communication skills, and verifiable prior employment as an Amazon engineer.

Set technical strategy for backend identity decisioning systems, lead major engineering initiatives, and ensure highly available operations. Partner with product, design, and analytics teams; establish coding, architecture, monitoring, testing, and on-call standards; communicate technical decisions; and mentor engineers. Build and scale distributed systems supporting KYC, user lifecycle management, and identity decisions across international markets.

Build and own Keycard’s infrastructure platform, including bring-your-own-cloud deployments, multi-tenant and single-tenant cells, infrastructure as code, Kubernetes operations, releases, upgrades, observability, security guardrails, and audited break-glass workflows. Develop tooling and agents for installation, diagnostics, and operational support. Define compatibility matrices, deprecation policies, and upgrade processes while partnering directly with enterprise cloud and security teams.

ML Engineering Intern supporting real-time markerless motion capture systems. Responsibilities include deploying and monitoring pose estimation models in cloud infrastructure, curating training data, running model experiments, diagnosing failures, and improving evaluation and deployment tooling. The role requires 3D computer vision knowledge, Python, and deep learning familiarity, with opportunities to work on containers, orchestration, model optimization, and robotics middleware.

Build foundational AI/ML platform capabilities, including LLM inference, orchestration, evaluation tooling, and scalable ML systems. Lead improvements to LLM workload management, data access, and enterprise-grade experimentation. Research and implement state-of-the-art models, fine-tuning, distillation, and autonomous-agent evaluations while establishing reliable offline and online quality standards for AI products.

Develop and maintain backend systems and full-stack software using PHP and Node.js, including containerized microservices on AWS Kubernetes. Responsibilities include system design, coding, testing, code reviews, troubleshooting, deployments, production support, and technical guidance. The engineer collaborates with product owners, project managers, DevOps, QA, and data engineering teams throughout the software development lifecycle, emphasizing reliability, performance, maintainability, and automated testing.

Designs, develops, and deploys agentic AI systems for cybersecurity, including vulnerability analysis, threat detection, alert triage, secure code generation, and remediation recommendations. Builds RAG pipelines, LLM integrations, APIs, microservices, and cloud-based platforms. Implements AI guardrails, threat modeling, human-in-the-loop governance, adversarial testing, explainability, and performance optimization while integrating systems with SIEM, SOAR, vulnerability management, and endpoint security tools.

Lead the architecture and development of internal AI platforms, including LLM applications, agents, RAG systems, workflow automation, and reusable AI infrastructure. Build secure production solutions integrated with company systems, establish guardrails and responsible AI practices, evaluate models and vendors, monitor reliability and costs, and support adoption across product development teams. The role also includes technical documentation, knowledge sharing, and eventual on-call participation.

Build and operate AI-native engineering workflows, agentic coding systems, evaluation frameworks, guardrails, and customer-facing AI products. Design distributed systems and LLM pipelines using TypeScript, Go, AWS, and related cloud technologies. Lead architecture, code reviews, reliability practices, and end-to-end project delivery while helping engineering teams adopt AI tools and workflows. Interview candidates, document best practices, and continuously evaluate advances in foundation models and AI development tools.

Lead development and modernization of cloud-native Java and Spring Boot services on AWS. Drive migration from legacy systems, event-driven architecture, automated testing, documentation, production reliability, and secure API design. Build and operate production AI agents using Amazon Bedrock and related frameworks, with evaluation, guardrails, observability, cost controls, and human oversight. Mentor engineers, lead technical direction and code reviews, improve delivery practices, and collaborate across teams on customer data and enterprise applications.

Owns the production ML and GenAI platform, including training and inference pipelines, model serving, registries, monitoring, evaluations, agent gateways, observability, cost controls, and incident response. The role builds reusable deployment patterns, governs agent tools and access, partners with Cloud Engineering, Data Engineering, Security, and SRE, maintains platform architecture, and mentors engineers. Candidates need strong Python and SQL, production ML operations experience, cloud platform expertise, and familiarity with LLM, agent, and MCP architectures.

Leads the architecture, development, optimization, and production deployment of complex AI and machine learning systems. Builds scalable ML platforms, pipelines, and infrastructure; establishes engineering standards; guides responsible AI practices; and mentors engineers. Partners with product and leadership teams to shape AI strategy, communicates technical tradeoffs, and drives high-stakes initiatives focused on reliability, scalability, security, cost, and safety. The role requires deep generative AI, agentic systems, RAG, cloud, and production engineering expertise.

Lead the architecture, development, deployment, and adoption of production AI/ML systems within client environments. Build scalable ML platforms, agentic systems, RAG pipelines, and integrations; resolve complex technical challenges; establish responsible AI and engineering standards; communicate tradeoffs to stakeholders; support client enablement; and mentor engineers. The role requires deep expertise in generative AI, AWS, cloud infrastructure, observability, security, cost optimization, Docker, and Kubernetes.

Build and operate Supabase’s multi-tenant, multi-region Kubernetes container platform. Responsibilities include platform automation, controllers, internal tooling, CI/CD and rollout primitives, security automation, observability, SLOs, error budgets, incident response, operational readiness reviews, resilience design, and infrastructure cost optimization. The role participates in on-call rotations and guides product teams toward reliable operational practices.

Plans and executes full-scope red team and adversary emulation operations, including phishing, C2, privilege escalation, lateral movement, EDR/XDR evasion, Active Directory and cloud compromise, and social-engineering scenarios. Develops custom offensive tooling, collaborates with Blue Teams to validate detections, documents attack chains, and provides remediation guidance. Requires 5+ years of offensive security experience, advanced scripting skills, and recognized offensive security certifications, with bug bounty or CTF achievements preferred.

Own and scale application security across Redpanda’s C++ engine, Go control plane, Console, and Rust/Go SDKs. Responsibilities include threat modeling, secure design reviews, SAST/SCA/DAST, fuzzing, secure code review, PSIRT and vulnerability disclosure, software supply-chain security, security release gates, and security champions. The role partners closely with engineering to improve secure development practices, address vulnerabilities, and protect authentication, authorization, encryption, audit logging, and multi-tenant isolation.

Leads full-stack software development, solution architecture, performance optimization, security, testing, and code quality. Translates business requirements into scalable applications, contributes to planning, conducts code reviews, mentors developers, supports users, and collaborates with technical and business stakeholders. Requires extensive experience with C#, ASP.NET MVC, JavaScript frameworks, SQL Server, database optimization, secure development, testing, and TDD.

Design, build, and operate scalable backend services for payments, cards, spend management, and compliance. Develop APIs, microservices, event-driven systems, and database solutions using Go, Python, or Node.js/TypeScript. Integrate LLM capabilities safely into production systems, improve observability and CI/CD, participate in on-call rotations, and collaborate with globally distributed engineering, product, compliance, and data teams.

Lead architecture and hands-on development for a unified SaaS frontend platform. Design reusable UI frameworks, modernize legacy codebases, improve developer experience, and guide engineering teams on scalable patterns, tooling, security, and performance. Collaborate with engineering, product, and UX stakeholders, mentor senior developers, prototype solutions, and scale successful practices across products. The role requires backend and frontend expertise, cloud deployment, Kubernetes operations, distributed data stores, and technical leadership without people-management responsibilities.

Lead the technical direction, development, deployment, and maintenance of integrations connecting the Censys Platform with SIEM, SOAR, TIP, cloud, and vendor systems. Build scalable API connectors, maintain APIs and SDKs, establish CI/CD and testing standards, guide internal and external engineers, partner with vendors and business teams, and advance AI-agent-enabled integration workflows.

Build and operate applied AI systems, including LLM integrations, agentic workflows, data pipelines, evaluation frameworks, MCP tools, APIs, and governed content-serving layers. The role emphasizes production reliability, provenance, security, cost and latency management, regression detection, and human-reviewed AI outputs. Responsibilities include cloud deployment, structured content modeling, telemetry, governance, mentoring, and collaboration on AI standards. Strong Python, TypeScript, AWS, API, data pipeline, and production LLM experience are required.