Top AWS Jobs in Toronto

Lead enterprise architecture and solution strategy for tokenization and digital asset platforms. Define end-to-end architectures, governance, smart contract standards, custody and KMS integrations, Web3 frontends/backends, regulatory compliance, and security auditing. Mentor teams, author architectural records, and drive delivery alignment from design through production.

Lead company-wide technical strategy for software quality and reliability. Drive cross-team initiatives, establish architecture and engineering standards, build shared platform capabilities (release safety, automated validation, observability), improve testability and resilience, define reliability metrics, lead multi-team programs, and mentor engineers to raise organizational reliability practices.

Own technical relationships with enterprise EMEA customers, translating business and e-learning goals into long-term platform strategies. Advise executives on platform performance, SLAs, AI and automation features, release readiness, API workflows, and technical risks. Monitor platform health, lead proof-of-concept pilots, troubleshoot critical events, analyze support trends, and collaborate with account, product, and advisory teams to drive customer growth and success.

Build and maintain full-stack features across React web, React Native mobile apps, and Python/Django backend services. Design reliable REST APIs for intermittent connectivity, write well-tested code, contribute to architecture, mentor engineers, participate in on-call support, and improve development workflows. The platform serves construction workers on active job sites and uses PostgreSQL and AWS.

Build and operate secure, scalable agentic AI applications and workflows across Azure and AWS. Lead initiatives from discovery and architecture through production delivery, rollout, adoption, and operational handoff. Develop AI agents, APIs, integrations, RAG solutions, and reusable platform services; evaluate emerging technologies; establish governance, observability, and responsible AI practices; and coach teams on secure AI adoption. Partner with business, engineering, security, governance, and risk stakeholders to deliver measurable value.

Build and own full-stack features for a cross-border banking platform, including React interfaces, backend services, APIs, payment integrations, ledgers, and financial systems. Improve scalability, security, performance, monitoring, testing, and reliability while collaborating with product, design, and engineering teams. Contribute to architecture, code reviews, technical decisions, mentoring, and production troubleshooting in an onsite Toronto startup environment.

Build and maintain performant, scalable full-stack SaaS systems using front-end and back-end technologies. Develop reliable features, write unit and integration tests, monitor performance, debug issues, review logs, and contribute to architectural decisions. Own features from technical design through deployment and post-release support while documenting solutions and collaborating with engineering, UX, and product teams.

Build and operate ingestion, reconciliation, and reporting systems that reconcile card-network and partner settlement files against internal transactions. Deliver end-to-end features for traceability, accounting journals, tax and regulatory reporting, and compliance tooling while ensuring reliability, scalability, and data privacy.

Design and build reusable, metadata-driven data and AI engineering frameworks using Databricks and cloud-native services. Architect and deploy Delta Live Tables, Lakeflow, and orchestrated Databricks workflows for automated data processing and AI agent deployment. Implement RAG/Graph RAG solutions and agent orchestration (AgentOps, Agent-bricks, LangGraph), integrate real-time streams, manage AI agent lifecycles, and ensure governance via Unity Catalog or AWS Glue.

Own the technical path for moving internally built AI agents from prototype to enterprise production. Define production-readiness standards, automated evaluation and regression testing, architecture patterns, deployment templates, and secure identity and data access practices across Databricks and AWS. Build reference implementations, diagnose agent failures, optimize latency and token costs, lead design and readiness reviews, manage operational handoffs, and guide business teams through platform and data decisions.

Lead application systems analysis and programming, integrating multiple functions, resolving complex problems, and advising mid-level developers while ensuring compliance with regulations.

Own full-cycle recruiting for forward-deployed engineering and leadership roles across the US. Source passive AI and technical talent through LinkedIn, GitHub, communities, referrals, and talent mapping. Build Provectus’s talent community through meetups, workshops, hackathons, university programs, and industry events. Partner with hiring managers and senior leadership on hiring criteria, workforce planning, compensation benchmarking, candidate experience, and offer negotiation.

Build, maintain, and evolve Mozilla’s Internal Developer Infrastructure Platform: CI/CD, observability, Kubernetes optimization, environment bootstrapping, and cost optimization. Lead technical decisions, create Claude skills and agentic workflows to boost developer productivity, mentor teammates, write proposals, and deliver cross-functional platform features while ensuring usability, maintainability, and safe production paths.

Leads customer-facing digital forensics and incident response engagements, directing investigations, prioritizing containment and remediation, delegating analyst work, and coordinating with legal and insurance stakeholders. Conducts root cause and data-exfiltration analysis, manages multiple incidents, identifies MITRE ATT&CK techniques, mentors analysts, and produces executive reports with remediation guidance. The role focuses on complex incidents including ransomware, network breaches, business email compromise, insider threats, and cloud environments.

Build and own backend services, ERP and supplier integrations, AI agentic workflows, spend analytics, and customer-facing procurement features. Develop evaluation and monitoring systems for production AI, establish architecture, improve security and privacy, and mentor junior engineers. Collaborate directly with customers and founders to shape product direction in an early-stage environment.

Design and deliver backend software automating post-trade processing, including clearing, settlement, reconciliation, and depository integrations. Contribute to technical architecture, mentor developers through pairing and code reviews, and collaborate with Securities Operations, Product Management, and engineering teams. Build accurate, auditable systems for high-stakes financial workflows while expanding pipelines for new products.

Design, develop, deploy, and optimize generative AI applications using LLMs and AI frameworks. Build scalable APIs, integrate foundation models and third-party platforms, automate business workflows, and connect AI solutions to internal systems and data sources. Monitor model quality, improve prompts and configurations, support responsible AI governance and security, evaluate emerging technologies, and collaborate with product, engineering, and business teams.

Designs and maintains Retrieval-Augmented Generation systems, including document ingestion, indexing, semantic search, vector databases, embedding pipelines, and LLM integrations. Develops APIs and scalable knowledge systems, optimizes retrieval accuracy and performance, monitors production environments, and implements governance and security controls. The role also evaluates emerging retrieval technologies, troubleshoots indexing issues, documents procedures, and collaborates with AI engineers and product teams.

Leads the design, development, and optimization of enterprise Retrieval-Augmented Generation platforms. Responsibilities include building retrieval architectures, ingestion and indexing frameworks, semantic and vector search systems, evaluation methods, scalable APIs, and LLM integrations. The role also covers security, governance, compliance, monitoring, troubleshooting, documentation, technical leadership, mentoring, and evaluating emerging AI retrieval technologies.

Leads the architecture, development, deployment, governance, and optimization of enterprise Generative AI solutions. Builds applications using LLMs and foundation models, develops prompt engineering and evaluation strategies, and ensures secure, scalable production deployments. Partners with executives and business leaders to define AI roadmaps, mentors engineering teams, evaluates emerging technologies, and drives proof-of-concept initiatives and responsible AI practices.

Manage and optimize infrastructure supporting AI and machine learning systems, including cloud environments, GPU/CPU clusters, model deployment, data pipelines, security, monitoring, troubleshooting, automation, and CI/CD. The role develops scripts, maintains documentation, supports AI teams, and improves system reliability and scalability. Candidates need a relevant bachelor's degree, at least two years of systems administration or infrastructure experience, cloud and containerization expertise, scripting skills, and familiarity with machine learning frameworks and deployment pipelines.

Oversee and optimize infrastructure supporting AI and machine learning platforms, including servers, cloud environments, compute resources, GPU/CPU allocation, security controls, monitoring, and incident response. Design scalable AI environments, automate operational processes, troubleshoot system issues, and support model deployment and training workloads. Mentor junior administrators, evaluate emerging technologies, and recommend improvements to AI platform architecture, reliability, security, and scalability.

Drive and close net-new and expansion sales within MongoDB's strategic accounts. Build and qualify pipeline, cultivate executive and multi-level relationships, execute upsell/cross-sell strategies, meet quarterly quotas, and collaborate with Customer Success, Professional Services, Product, and Marketing to drive adoption and satisfaction.

Deploy onsite with strategic customers to understand security and risk workflows, then build AI-powered integrations, data pipelines, and production software. Translate security ratings into financial risk models, connect systems across cloud environments, rapidly prototype and harden solutions, and guide technical relationships through renewal and expansion. The role bridges customers, Product, Engineering, Customer Success, and Sales Engineering while feeding field insights into the product roadmap.

Design, build, and operate services powering card issuance, transaction authorization, wallet provisioning, 3-D Secure, and disputes. Own features end-to-end within a microservice, event-driven architecture, write well-tested code, participate in reviews, and perform operational ownership including on-call and incident response. Leverage AI developer tools to improve velocity and quality.