Top AI Jobs in Montreal

Lead the QA and UAT testing strategy for projects, ensuring quality assurance activities align with user requirements and industry standards while fostering client relationships.

Zapier seeks an Engineering Manager to lead the AI Capabilities team, focusing on building a robust AI platform with reusable APIs and high-quality features. Responsibilities include leading engineers, making architectural decisions, and enhancing development processes to improve user experiences and align with company goals.

Lead a team of engineers to design and implement AI features, improve user experience, and drive automation solutions with a focus on user needs and experimentation.

The AI Research Infrastructure Engineer will design, build, and implement AI systems and automated data pipelines for research, focusing on workflow automation and integration with tech stacks.

The role involves building production-grade agent systems, designing scalable backend services, and collaborating with engineering teams on AI tools and blockchain technology.

Manage a team of engineers, oversee project execution, partner with Product Managers, ensure high-quality software and Agile practices, and drive AI integration efforts.

The Director of AI Engineering will lead Apollo's AI strategy, oversee the development of AI systems, and manage the AI Engineering team, ensuring alignment with business goals and enhancing engineering excellence.

As a Senior Engineer at 8am, you will design and implement AI features, collaborate with teams, and develop tools for AI deployment.

The Staff Developer Relations Engineer will teach and engage developers with AI tools through demos, workshops, content creation, and community programs, while collaborating across teams to enhance projects and programs.

The Senior Architect will design platforms for Generative AI, develop solutions, maintain architecture governance, collaborate with teams, and manage integration strategies.

Design, develop, and maintain AI systems with a focus on NLP, recommendations, and generative algorithms while working in an agile team. Ensure operational quality and automate development workflows.

Contribute to fullstack features using JavaScript, Ruby, and Python, collaborating with teams for development, bug fixes, and performance enhancement.

Develop and maintain web interfaces, validate AI-generated code, provide UI/UX feedback, and follow project guidelines in a remote setting.

The role involves developing features for GitLab's AI-powered platform, advocating for quality improvements, and collaborating within a remote team.

Drive the development of gameplay AI systems for Unreal Engine 6, collaborating with teams to transition current systems, optimizing performance, and enhancing user experience.

As a Tech Lead in AI Software Development, you will lead system design, mentor developers, ensure quality delivery, and collaborate with cross-functional teams to deliver impactful AI solutions.

Develop AI systems for in-game characters, collaborate with game designers, optimize performance, and create debugging tools in C#. Requires extensive experience in game programming.

The Senior Software Engineer will develop and optimize backend systems for AI applications, focusing on text-to-speech technology, managing cloud infrastructure, and ensuring high availability.

Design and develop scalable data infrastructure for AI platform, optimize database performance, create data pipelines, and mentor junior engineers while collaborating across teams.

As an AI & Automation Engineer, you'll design and optimize automation workflows, integrate systems, and develop AI tools to enhance operational efficiency.

The Research Engineer will develop the DeOS framework, optimizing AI workloads, managing GPU resources, ensuring scalability, and contributing to open-source projects.

Design, implement, and maintain gameplay systems in Unreal Engine, collaborating with teams to optimize performance and document code.

As a Senior Full Stack Engineer, you'll develop user-facing features and back-end infrastructure for AI agents, ensuring quality and scalability in a collaborative environment.

The role involves building end-to-end distributed LLM inference deployments, collaborating on features, and developing scalable ML infrastructure tools.

Develop web-based and data-heavy applications using modern technologies. Collaborate with teams and stakeholders to enhance solutions and integrate AI features.