Top AWS Jobs in Toronto

Build and prototype state-of-the-art large language models and AI assistants, take services from ideation to production, fine-tune open-source models, use LLM platforms, analyze model statistics and projections, and collaborate cross-functionally in a fast-paced hybrid Palo Alto environment.

Build and maintain backend systems and infrastructure for an AI-focused startup. Implement services in Python/TypeScript, design moderately complex systems, manage SQL/NoSQL databases, deploy on public cloud (AWS/GCP/Azure), use containerization and IaC, build CI/CD pipelines, implement monitoring/logging (Prometheus/Grafana/ELK/DataDog), and ensure strong networking, Linux, and database security.

Trusted technical advisor for strategic customers to deploy, operate, and scale LanceDB. Lead onboarding, architecture reviews, and performance tuning; troubleshoot distributed-system issues; build dashboards and enablement; collaborate with sales engineering and product/engineering to influence roadmap and resolve escalations; contribute runbooks, tooling, and processes for post-sales success.

Lead technical partner enablement and co-delivery for LanceDB: onboard and train partner engineers, run PoCs and architecture reviews, author reference architectures and deployment blueprints, relay partner feedback to product, and support GTM and thought-leadership activities to scale partner integrations.

Design and build a test automation framework from scratch; write and maintain API, integration, and end-to-end UI tests; integrate suites into CI/CD; reduce flakiness; apply and evaluate AI-assisted testing tools; mentor engineers and raise automation quality across squads.

Senior individual contributor owning AI/ML products end-to-end: discover opportunities, define roadmaps, make build-vs-buy decisions, lead vendor evaluations and cross-functional delivery, monitor KPIs and operational signals, and drive continuous improvement and adoption.

The Forward Deployment Engineer will deploy and optimize Generative AI solutions, architect scalable infrastructure, and implement data pipelines across multi-cloud environments while collaborating with cross-functional teams to maximize business value.

Design, build, deploy, and optimize AI/ML models and pipelines (including LLMs and RAG). Integrate models into production, work with cross-functional teams, ensure monitoring and compliance, and contribute to scalable AI systems and automation.

Design, build, and operate scalable, secure cloud infrastructure and CI/CD pipelines. Manage Kubernetes environments, implement IaC (Terraform), improve observability, troubleshoot production issues, support security/compliance, and mentor junior engineers to optimize platform reliability and cost.

Design, build, fine-tune, and deploy production-grade AI and ML systems (LLMs, RAG). Develop scalable APIs, integrate AI into business systems, monitor model performance, and collaborate with cross-functional teams.

The Detection & CorpSec Engineer at Quora will build and maintain detection systems, strengthen corporate security controls, and respond to security incidents while collaborating with IT and advising non-technical teams.

Develop scalable applications and backend infrastructure for brand safety and content suitability. Design APIs, deploy ML models, and manage database systems.

Seeking a Sr DevOps Engineer to design and maintain secure, scalable AWS cloud infrastructure, manage CI/CD pipelines, and improve system reliability across advanced data analytics platforms.

Lead high-impact AI solutions, overseeing architecture and deployment. Partner with stakeholders, coach engineers, and drive project success from inception to launch.

The Principal Software Engineer at Snout will define architectural direction, build features across the stack, and enhance team effectiveness, ensuring quality and performance.

As a Senior Software Engineer, you'll develop and enhance the Snout web platform, collaborate with teams, and lead technical projects while ensuring performance and reliability.

The role involves building and scaling growth engineering products, balancing quality and speed effectively, and influencing product direction and AI-driven growth solutions.

As an ML Engineer at Luma AI, you will design high-performance model serving pipelines, manage GPU resources, and optimize CI/CD for large-scale AI systems.

As a Software Engineer in Reliability, you'll architect and manage multi-cloud GPU infrastructure, ensuring performance, security, and scale while debugging complex hardware/software issues.

Lead and mentor a team of Forward Deployed Engineers to deploy Cohere's North platform into private cloud and on-premises environments. Own technical delivery and customer success, collaborate with Product/Engineering/Sales, optimize K8s services (OpenSearch, databases), define GPU/CPU scaling, and build scalable processes and tooling.

Contribute to the reliability and performance of Mithril's GPU orchestration platform through automation, observability, and infrastructure management. Collaborate with the team to ensure scalability across multi-cloud environments while maintaining systems stability and implementing SLOs.

The Frontend Engineer will build web interfaces for a healthcare APIs clearinghouse while collaborating across teams to ensure high-quality, responsive design and functionality.

As a Reliability Engineer, you'll design, implement, and maintain AWS cloud environments, ensuring systems' reliability and performance, while enhancing monitoring and incident response capabilities.

Design, build, and deploy AI-powered legal applications using LLMs, RAG, and agentic AI. Partner with legal stakeholders to translate workflows, build document ingestion and vector search pipelines, ship production-grade features, implement governance/security controls, and mentor/legal users while aligning solutions with enterprise architecture and monitoring quality and evaluation metrics.

Design, build, and maintain scalable distributed data pipelines and a secure data lakehouse for streaming and batch processing to support real-time analytics, ML, and experimentation. Automate, test, and harden workflows, architect logical and physical data models, build ML model features, and collaborate with product, analytics, and data science teams to turn data into value.