Top AI Jobs in Ottawa

Lead development and implementation of AI legal and compliance frameworks. Conduct cross-functional AI reviews, maintain governance docs (model cards, impact assessments), partner with engineering on transparency and fairness requirements, build incident response and monitoring processes, and scale AI legal review workflows.

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

Build, deploy, and maintain AI-powered agents, APIs, and applications for People operations on Snowflake. Integrate Workday/Notion, ensure security and data governance, design multi-model LLM reliability, and own production lifecycle including CI/CD, monitoring, and incident response while working with non-technical stakeholders.

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

Ownership of backend features for Agentic Tools: design and implement GraphQL/REST APIs, build secure scalable Ruby on Rails services, improve RSpec automated tests, collaborate across product and AI teams, participate in Tier 2 on-call, and shape architecture for AI agent interactions with GitLab.

Lead end-to-end product design across web and native, run user research, build rapid AI-enabled prototypes, write production frontend code, evolve the design system, and partner with PMs, engineers, and analysts to deliver delightful, outcome-driven experiences for parents.

Own end-to-end product experiences across web and native mobile: set design vision, run user research, rapidly prototype (AI-enabled), write production frontend code, evolve design systems, mentor designers, and drive measurable product outcomes.

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.

Support and enhance ADI's global compliance program aligned to enterprise risk and ISO 37301. Monitor regulatory developments, update policies, execute risk‑based monitoring and testing, manage investigations and remediation, support audits, develop training and communications, track metrics, and collaborate with Legal, HR, Finance, IT, and Operations to drive continuous improvement.

Lead and support execution of ADI's global compliance programs: risk‑based monitoring, policy management, testing, investigations, training, ISO 37301 alignment, audits, remediation, and cross‑functional stakeholder coordination.

Produce provocative, research-driven insights and advisory for CFOs, develop thought leadership on finance transformation and AI, deliver client-facing presentations, support sales and peer review, mentor junior analysts, and drive research process improvements to influence finance strategy and market agendas.

Own and improve the AI Social generative media pipeline: build and ship LLM-driven prompts/agent loops, choose and compose media (image, video, voice, music), collaborate with product/design/content, and deliver polished, production-quality social assets.

As a Principal Data Engineer, you will design and implement LLM, AI-powered security data platforms, mentor engineers, and drive the adoption of data solutions across teams.

As a Sr. Applied AI Engineer at Zapier, you will build and enhance AI platform capabilities, focusing on LLM Ops and ML Ops to support scalable AI development across teams.

As Principal Product Manager, you'll define product strategy for GitLab's AI models, focusing on custom and self-hosted AI capabilities, collaborating with engineering, and ensuring alignment with company strategy.

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 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 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.

Drive independent, high-impact research on frontier AI topics, publish findings, collaborate widely, and mentor new researchers in the field.

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 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.

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

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 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.

Evaluate AI-generated coding interactions end-to-end, judging usefulness, high-level correctness, engineering judgment, and quality of explanations. Provide clear, opinionated feedback, distinguish response quality levels, and help define best practices for AI-assisted developer workflows.