Top AI Jobs in Ottawa

Lead end-to-end feature development and modernization across diverse SaaS stacks, using AI-augmented tooling to accelerate delivery. Maintain legacy production stability, establish testing and secure deployment workflows, and mentor junior engineers while partnering with business stakeholders.

Lead end-to-end feature development across diverse web stacks with a front-end emphasis, modernizing legacy SaaS applications while ensuring stability, test coverage, and secure deployments. Champion AI-augmented development practices and agentic coding tools to accelerate delivery, mentor engineers, and communicate progress to leadership.

Senior front-end–leaning full-stack developer to own end-to-end feature delivery across diverse SaaS stacks. Use AI-augmented development tools to accelerate builds, modernize legacy systems, ensure test coverage and safe deployments, mentor junior engineers, and communicate progress to leadership while delivering polished, user-focused UI/UX.

Configure and maintain legal technology platforms, build integrations and automated workflows, develop AI-assisted tools and dashboards, analyze operational data, and drive process improvements across the legal function while collaborating with IT, Product, and AI teams.

Lead and build Burq's data and ML platform: own warehouse, ingestion, semantic layer, and BI; architect and ship forecasting, optimization, matching, and reliability models; implement feature pipelines, training/serving, and monitoring; drive AI agent initiatives; hire and grow a data/ML team; set governance, intake processes, and partner with product, engineering, and commercial stakeholders to align models with business impact.

Build and prototype state-of-the-art large language model systems and deep AI assistants, take services from ideation to production, fine-tune and compare open-source and commercial LLMs, use cloud platforms, analyze model and projection accuracy, and collaborate cross-functionally.

Provide embedded legal counsel to product and engineering on privacy, IP, and consumer protection; lead Arena's global privacy program (GDPR, CCPA/CPRA, cross-border transfers); advise on AI governance and responsible deployment; support commercial contracts and data flow frameworks; build privacy processes, playbooks, internal agents, and legal-tech integrations to scale operations.

Build and operate large-scale, high-performance infrastructure to run AI inference and agents on real-time streaming data. Design scalable, reliable serving layers, solve distributed-systems challenges, and optimize across cloud providers.

Founding technical role to design, build, and deploy internal AI tools and agents using LLMs and retrieval systems. Partner with investors and the data team to accelerate sourcing, diligence, portfolio insights, and define CRV's AI strategy through rapid prototyping and production-grade systems.

The AI Strategy Consultant will drive marketing strategy using AI, work with teams to implement solutions, and enhance customer outcomes.

The role involves rapid prototyping and architecting production LLM pipelines for AI in marketing workflows. Responsibilities include developing proofs of concept, making AI architecture decisions, building production data agents, and understanding user workflows for seamless AI experiences.

Build and ship AI-driven GTM systems: outbound lead engine, inbound/lifecycle automations, AI-assisted content pipeline, data/reporting layer, and fast production patches to eliminate manual marketing workflows.

Design and implement advanced generative AI models, optimize deployment in production, collaborate with teams, and stay updated on AI advancements.

Drive the expansion of software development and AI services by managing client relationships, building sales pipelines, and negotiating contracts.

Lead large-scale AI infrastructure deployment programs, manage project execution for GPU clusters and data center infrastructure, and mentor junior project managers.

The engineer supports simple data hall designs, drafts AutoCAD drawings, assists with capacity planning, and learns data center principles under supervision.

Lead AI feature design and delivery, build large-scale ML systems, mentor engineers, and define best practices across the engineering organization.

Lead live sessions, office hours, and project reviews for senior engineers transitioning into system architecture. Provide detailed architecture critiques, mentor students one-on-one, run architecture review boards, record instructional videos, conduct mock interviews, and coordinate supporting instructors to ensure high technical standards and student readiness for architecture roles.

Author and refine a systems-engineering curriculum for experienced engineers: shape syllabus, define learning objectives, produce and fact-check AI-assisted lessons, design hands-on projects, write rubrics and reviewer guides, and iterate with an LX designer. Part-time (15-20 hrs/week) for ~4-6 months.

Own end-to-end design, build, deploy, and iterate on production AI systems that improve lead quality, reduce refunds, and automate workflows. Ship customer-facing AI features, agentic workflows, retrieval and context layers, reliable event-driven automations, evaluation datasets and monitoring, and reusable components while connecting technical decisions to business KPIs.

Lead Moonshot's applied AI safety portfolio, conducting red teaming and adversarial evaluation across online-harm categories, advising AI companies and regulators, managing clients and staff, and growing the portfolio through methodology, business development, and external engagement.

Design, build, and optimize distributed PyTorch training systems for multi-node GPU clusters. Profile and eliminate performance bottlenecks across data pipelines to GPU kernels, implement low-level CUDA/cuDNN/Triton kernels, tune CPU/GPU/memory/network utilization, and develop monitoring and debugging tools for large-scale training runs.

As a Senior Agentic AI Engineer, you will lead innovation in AI initiatives, from ideation to prototyping, collaborating with teams to deliver impactful solutions.

Lead development of back-end systems and end-to-end data/ML pipelines, build and deploy generative AI workflows, design and consume performant APIs, contribute to AI/ML architecture, mentor engineers, and communicate progress to leadership.

Develop and maintain backend services and data pipelines to support AI/ML and GenAI workflows. Build and consume secure, scalable APIs, work with microservices on Kubernetes, collaborate on design and code review, and grow applied ML skills under senior mentorship.