Top AI Jobs in Ottawa

Lead design and delivery of AI and Generative AI solutions for telecom customers. Translate customer needs into scalable architectures, drive end-to-end implementation, support pre-sales, mentor teams, and provide thought leadership on AI strategy, governance, and deployment across CSP ecosystems.

Lead design and development of AI-enhanced baseband solutions across PHY/L1, L2 and scheduling. Identify AI opportunities, prototype and evaluate ML algorithms, optimize for RAN compute and accelerators, validate via simulation/lab/over-the-air, collaborate cross-functionally, scout technologies, and mentor peers to bring prototypes into deployable solutions.

As a Senior Data Scientist, you will lead AI risk assessments, coordinate with stakeholders, and ensure effective AI governance practices for safe financial application deployment.

As a Staff Machine Learning Engineer, you'll lead AI initiatives using large-scale data, optimize ML models for edge devices, and collaborate with cross-functional teams.

Lead teams in building end-to-end AI products focused on computer vision and sensor data. Oversee model lifecycle, data management, and deployment. Drive innovation in AI while fostering team development and ensuring customer success.

Lead architecture and technical strategy for AI-driven product quality systems using LLMs and agents. Build scalable evaluation frameworks, detect regressions, generate insights, and drive cross-functional adoption while mentoring engineers and defining standards for trustworthy AI.

Lead an interdisciplinary research program investigating mechanistic interactions in psychosis using neuroimaging, computational psychiatry, AI/ML, and multimodal data integration; supervise trainees, obtain external funding, teach, and translate discoveries into precision psychiatry and patient-centred care.

Lead strategy and technical execution for a global, AI-native learning ecosystem. Design AI-powered learning experiences, own multi-year platform roadmap, drive AI fluency programs, partner with People Technology and Analytics, and manage vendor/compliance activities to measure adoption and impact.

Lead the development of an AI framework for endpoint security, collaborate with teams, optimize solutions, and ensure model efficiency across various platforms.

Lead and own the end-to-end Registry design experience, set vision and KPIs, hire and develop a small design team, use AI-native workflows to prototype and ship cross-platform consumer products, partner with PMs/engineers/analytics, and scale design systems and processes to drive measurable business impact.

As an AI Engineer, you will develop and maintain data pipelines, manage data integration, and enhance data quality for analytics projects.

Build and evolve core AI platform capabilities: design shared execution patterns, APIs, and backend services to support multi-step GenAI workflows, model integrations, and scalable production systems. Collaborate with AI engineers, data scientists, and product partners to turn emerging AI use cases into reliable, extensible platform features.

The role involves leading product development for Video-based Safety using AI and ML, defining roadmaps, and collaborating with engineering and sales teams to enhance safety solutions.

The Enterprise Architect will design data-centric systems, lead technical policy implementation, and collaborate with partners while addressing cybersecurity and system architecture requirements.

Lead design and delivery of production autonomous agents and agentic workflows: architect orchestration, integrate and optimize frontier LLMs, drive model fine-tuning and evaluation, mentor engineers, and translate business goals into safe, reliable, scalable AI-powered product experiences.

The Director of Performance Marketing will lead paid marketing strategy, focus on AI integration, manage a high-performing team, and ensure alignment with business objectives.

As a Software Engineer II, you'll develop generative AI applications, enhance sales operations through AI tools, and collaborate with engineering teams on a remote basis.

Lead large-scale, cross-functional engineering projects to deliver AI-native SaaS features. Own platform architecture for multi-tenant, cloud-native systems, drive engineering standards and developer tooling, mentor engineers, and operationalize LLMs, agent frameworks, and real-time data in production.

Teach an asynchronous virtual seminar/workshop on AI, coding, and robotics for elementary education. Deliver course content, apply Ontario curriculum, assess learners, and maintain professional standards. Course runs Nov 17–Dec 8, 2026; expected enrolment 30.

Lead multi-year vision, strategy, and roadmap for Webflow's agent surfaces and AI platform. Align cross-functional teams, set quality standards and guardrails for non-deterministic agent experiences, serve both developers and creators, prototype and ship product improvements (including contributing code), and mentor PMs while measuring and iterating on outcomes.

Build end-to-end full-stack AI features on a Ruby on Rails backend and React frontend, design and run experiments (A/B tests and AI evaluations), implement AI infrastructure for reliable LLM inference, and iterate quickly to scale production-ready LLM-powered products.

As an AI Transformation Owner at GitLab, you will shape AI strategies, improve workflows, and drive adoption of AI solutions in your organization, working closely with an AI Engineer.

The role involves leading as a thought leader in technology operations and AI implementation, mentoring executives, advising on technology strategies, and producing actionable insights for clients.

Build and maintain backend features for Agent Observability across the GitLab monolith and AI gateway. Design and implement GraphQL/REST APIs, improve automated tests (RSpec), collaborate cross-functionally, troubleshoot production issues on Tier 2 on-call, and help shape responsible AI integration and platform architecture.

Design, build, and ship production AI systems for Acquia DAM. Architect agentic, stateful multi-agent workflows, own AI observability and RAG architectures, evaluate LLM providers and tooling, mentor engineers, and ensure enterprise-grade security, scalability, and compliance for AI features.