Top AWS Jobs in Toronto

Leads the architecture and hands-on evolution of Kinaxis Maestro’s cloud-native core platform. Designs distributed database, storage, query execution, and data-processing systems for massive scale, elasticity, performance, resilience, and cost efficiency. Guides architectural standards across Product R&D, prototypes and implements solutions, mentors senior engineers, and influences technical direction across teams. Requires deep expertise in distributed production systems, cloud platforms, concurrency, consistency, and high-performance backend development using Go, C++, or similar languages.

Owns the vision, strategy, roadmap, and outcomes for major Maestro platform capabilities. Leads customer discovery, platform architecture decisions, AI agent capabilities, data product integration, developer tooling, and enterprise adoption. Partners with engineering, data, cloud, and go-to-market teams to deliver scalable APIs, SDKs, services, and administration experiences. Requires 8+ years of product management experience, strong AI and modern data platform knowledge, technical fluency, and the ability to influence senior stakeholders.

Designs and operates scalable AWS infrastructure, builds containerized systems with Docker and Kubernetes, manages Terraform infrastructure as code, improves Jenkins and GitHub Actions CI/CD pipelines, and implements monitoring, logging, troubleshooting, and root cause analysis. The role partners with engineering teams to improve reliability, security, performance, and deployment workflows in complex SaaS environments.

The role involves leading financial analysis for technology spend, designing data models, developing visualizations, and ensuring data quality, while collaborating with internal stakeholders across the company.

Lead design and delivery of scalable, secure solution architectures for Capital One's travel portal. Create ADRs and HLDs, participate in governance, maintain capability catalogs, evaluate new technologies, collaborate with stakeholders, mentor teams, and ensure solutions meet architectural and security standards in a regulated environment.

Build and operate scalable data pipelines supporting batch and streaming workloads, AI and ML systems, analytics, and sports intelligence products. Responsibilities include ETL/ELT development, data modeling, orchestration, retrieval pipelines, data quality, lineage, monitoring, infrastructure optimization, and collaboration with ML, product, and analytics teams.

Designs and operates scalable ML infrastructure, including training, serving, inference, orchestration, and deployment pipelines. Optimizes GPU and CPU workloads, manages cloud clusters, implements observability and SLOs, and secures automated CI/CD and infrastructure-as-code workflows. Partners with research and AI engineering teams to productionize models, while building reusable tooling, templates, and runbooks. The role also supports low-latency model serving, data quality, model promotion, and cost optimization.

Build and operate production systems for Juniper Square’s AI Platform, including APIs, infrastructure, reusable frameworks, and AI/LLM capabilities. Own projects end to end, ensure reliability and observability, contribute to technical design, and support incident response. Collaborate with product, design, QA, and engineering teams while applying AI-native development practices. The role requires strong Python backend engineering, cloud-native technologies, databases, production operations, and hands-on experience with RAG, agents, evaluation workflows, or related AI systems.

Lead the system architecture for production agentic AI capabilities, including orchestration, state and memory, tool integration, reliability, and guardrails. Write technical design reviews and standards, prototype architectural decisions, and partner with Data Science and engineering teams. Influence technical direction, review architectures, mentor senior engineers, promote AI-assisted development, and communicate trade-offs to executives and customers.

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 scale AI-agent infrastructure, including orchestration, tool calling, context management, guardrails, evaluations, and LLM-as-judge pipelines. Develop Python and Node.js microservices, Laravel APIs, AWS infrastructure, and optimized MySQL systems. Diagnose agent failures, improve prompts and tools, maintain reliability and observability, contribute to architecture, review code, collaborate cross-functionally, and mentor engineers.

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.

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, 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.

Design, build, and operate CIAM backend services for registration, authentication, authorization, and account lifecycle. Implement OAuth/OIDC/SAML/SCIM, develop APIs in Python/Kotlin, integrate identity platforms, automate deployments with IaC and CI/CD, and ensure secure, reliable, high-scale identity operations.

Build the enterprise platform for an AI creative product, including workspaces, permissions, SSO, audit logs, usage-based billing, invoicing, payment integrations, and security foundations. Develop backend services, APIs, and polished React/Next.js administrative interfaces for multi-tenant customers. Collaborate with founders, sales, and customers to define and ship enterprise features while taking increasing ownership of platform surfaces.

Lead and grow a Maps & Search engineering team responsible for the home search experience across web and mobile. Stay hands-on with coding and architecture, scale a high-throughput search platform (~1B monthly requests), improve relevance and latency, integrate AI/LLMs into discovery and ranking, shape product roadmap, and own delivery, SLOs, and incident response.

Consultative Sales Engineer responsible for driving complex sales opportunities, designing cloud data security and data risk management solutions, delivering proof-of-value engagements and technical demonstrations, advising executive stakeholders, supporting partner channels, and coordinating cross-functional resources. The role requires cloud security, IAM, networking, databases, encryption, privacy, data governance, and strong technical communication skills.

Lead architecture and delivery of a composable, API-first Core Commerce Platform powering checkout, payments, OMS, inventory availability, loyalty, and post-order experiences. Drive integration of packaged solutions (SFCC, Fluent Commerce), payments, social and agentic commerce, and AI-native capabilities. Own SLAs, reliability, security (PCI-DSS), roadmap, and engineering standards while building and leading high-performing, on-site platform teams.