Top AWS Jobs in Toronto

Seeking an experienced Frontend Engineer to enhance user experience for multimodal data management applications. Responsibilities include optimizing user interfaces, collaborating with designers, and building scalable backends.

Lead advanced analytics and ML projects: perform EDA, feature engineering, statistical modeling, and build/optimize production ML and Generative AI solutions (LLMs, embeddings, RAG). Collaborate with engineering and MLOps to deploy and monitor models, create visualizations and dashboards, translate insights for stakeholders, and mentor junior team members.

Own and ship end-to-end full-stack features for a high-scale, AI-native publishing marketplace. Design APIs, optimize queries, deploy cloud-native apps, build LLM-powered capabilities, ensure security and privacy compliance, mentor engineers, influence architecture, and maintain reliability and performance under load.

The Software Engineer will design and implement systems across the stack, focusing on AI-native engineering principles, distributed systems, and high-performance data processing.

As a Backend Engineer at Trust Wallet, you'll develop backend features for multiple platforms, maintain infrastructure, ensure system monitoring, and foster team growth.

Lead and grow a platform engineering team focused on Infrastructure, SRE, and DevOps. Set technical direction, deliver platform roadmaps, oversee cloud infrastructure and deployment pipelines, build AI-powered internal tools, improve developer experience, own incident management and on-call sustainability, and collaborate with product, security, and compliance to ensure secure, scalable, observable systems.

Build and scale the internal developer platform: create tools, services and automation (CI/CD, canaries, feature flags, access workflows) using IaC (Pulumi/Python) and Kubernetes to enable safe, fast shipping. Drive platform initiatives, mentor teams, lead AI tool adoption for engineering, participate in on-call and design/code reviews, and ship major improvements across the organization.

Proactively explore RevenueCat data to identify customer problems and opportunities. Translate ambiguous product questions into analyses and production-grade predictive/descriptive models. Partner with Product, Engineering, and Analytics to shape roadmaps, define experimentation and benchmarking approaches, deploy and iterate on models powering customer-facing features, and communicate insights across technical and non-technical audiences.

Design and advance core infrastructure for engineering, ensure Kubernetes reliability, automate operations, and support AI infrastructure.

Lead and manage AI teams to design, train, deploy, and operate production-grade ML and generative AI systems for security detection and response. Define AI roadmap, partner with product and platform teams, uphold secure MLOps practices, mentor staff, recruit talent, and deliver scalable, observable AI integrated into production cloud environments.

As a Staff Software Engineer, you'll lead software infrastructure development, drive AI innovation, mentor engineers, and collaborate across teams to build advanced applications.

Build and maintain end-to-end product features across the stack (AWS, Python, Postgres, React, HTML/CSS). Collaborate with product and design, participate in design meetings, automate processes, advocate best practices, and deliver modular, reliable, and extensible solutions.

Design, develop, and deploy generative AI solutions (LLMs, RAG, vector DBs, agentic AI) for financial applications. Build AI agents, implement RAG pipelines, fine-tune and evaluate models, collaborate with engineering to productionize models (CI/CD, containers, cloud), and communicate results to stakeholders.

The Software Engineer will build and maintain internal developer tools, optimize CI/CD pipelines, and collaborate across teams to enhance developer experience and infrastructure reliability.

The Staff Software Engineer will develop and deploy high-performance machine learning systems, optimizing large language models for production environments. Responsibilities include ensuring high availability and low latency for AI applications and working collaboratively across teams.

The Site Reliability Engineer will develop, deploy, and operate AI infrastructure, focusing on high-performance and scalable machine learning systems using Kubernetes and cloud platforms.

The Technical Lead will design and implement backend systems, mentor engineers, optimize performance, and manage cloud architecture while ensuring scalable and reliable data processing.

Develop and deploy AI solutions for enterprise clients, focusing on large language models and agentic systems integration while ensuring quality, scalability, and security.

As a Senior Data Scientist, you will design AI solutions, lead NLP implementation, and mentor junior staff while transforming data into actionable insights.

The Principal Advisor for Cloud Strategy is responsible for developing multi-cloud infrastructure strategies, hands-on engineering automation, and leading transformation initiatives while providing leadership and technical advice to executives and teams.

Lead and facilitate agile sprint and release processes across engineering teams: run sprint planning, track metrics, coordinate QA and bug fixes, control release scope and branching, run incident response and postmortems, manage cross-team projects, and report status to senior leadership.

Lead the technical vision and evolution of Faire's Growth Platform, focusing on communication systems, targeting capabilities, and developer tooling while mentoring engineers and enhancing developer experience.

Validate and review Generative AI and deep learning models, perform R&D on LLM evaluation and explainability, develop validation methodologies, apply statistical tests and benchmark models, and communicate findings to technical and non-technical stakeholders.

Lead design, build, and 운영 of CI/CD platform components: self-hosted GitHub Actions runners on GKE, ArgoCD GitOps topology, Helm charts, and Jenkins platform. Deliver self-service tooling and migrations, ensure pipeline reliability with Datadog and PagerDuty, manage artifacts and secrets, and collaborate globally to own architecture, incidents, and long-term maintenance.

Lead architecture and delivery of scalable SaaS and hardware-integrated security systems. Drive firmware/OS integration, edge processing optimization, AWS-based data ingestion and BI, enforce code quality and security (SOC 2), mentor engineers, and incorporate AI into products.