Top AI Jobs in Ottawa

As a Software Engineer in AI Infrastructure, you will design and develop core platform components, build APIs and services, enhance performance, and automate tooling while collaborating across teams and improving system reliability.

Design and optimize high-performance kernels and custom operators for attention, MoE, GEMM, quantization and collective communication across NVIDIA, AMD and AWS Trainium. Improve LLM inference runtimes, develop distributed training/inference solutions at scale, use compilers and SDKs (Neuron, Torch Dynamo, PyTorch/XLA), contribute to open-source, and publish technical findings.

Lead end-to-end delivery of enterprise AI engagements: run discovery, size value, define features, coordinate cross-functional teams, drive adoption and change management, and own commercial expansion and program outcomes in regulated environments.

Part-time AI Application Engineer focused on measuring and improving healthcare AI agent performance in production. Responsibilities include instrumenting agent quality, identifying failure points, designing experiments, analyzing results, and partnering with clinical experts to improve patient outcomes. The role requires Python, strong analytical judgment, comfort with ambiguity, and interest in healthcare. Clinical experience is valued but not required. Engagement is structured as a paid internship or contract role with weekly Bay Area office syncs.

Lead executive interviews, stakeholder workshops, and current-state assessments to identify operational gaps and opportunities. Develop business cases, operating models, governance, KPIs, roadmaps, and practical AI-enabled solutions. Advise on AI strategy, innovation, commercialization, customer experience, and change management. Support transition of recommendations to delivery teams and occasionally participate in business development.

Embed with client engineering teams to deploy, integrate, and scale enterprise generative AI solutions. Build agentic systems, train and serve LLMs with Vertex AI, integrate models with BigQuery/AlloyDB/Spanner, implement streaming inference pipelines, and ensure operational reliability while collaborating with stakeholders to translate business goals into production-grade architectures.

Build high-performance API services and production-grade Generative AI systems. Design and implement end-to-end RAG pipelines, vector stores, embeddings, and latency-optimized generators. Develop agentic AI using LangChain/CrewAI/Semantic Kernel, integrate LLM APIs and AWS Bedrock, manage CI/CD and ML pipelines, and apply evaluation frameworks to detect hallucinations and optimize performance.

The Principal Cloud DevOps Engineer is responsible for deploying and maintaining IFIaaS applications in hybrid cloud environments, ensuring reliability, managing CI/CD pipelines, and providing on-call support.

Lead end-to-end AI-first transformation for operations: define problems, design architecture, build agentic systems and automation pipelines, integrate with CRMs and operational tooling, ship production solutions and drive company-wide AI adoption.

The AI Engineer designs, develops, and deploys AI-driven applications for non-data scientists, enhancing decision-making and model accessibility for organizations.

Lead architecture and performance exploration for next-generation AI inference accelerators. Build analytical models and simulation frameworks, define subsystem interfaces and roadmaps, and drive hardware/software co-design with compiler, runtime, ML, and silicon teams to optimize latency, throughput, memory, interconnects, power, and cost for evolving LLMs and transformer-based workloads.

The Senior AI Compute Infrastructure Engineer will manage and optimize GPU and accelerator clusters for AI workloads, collaborate with teams, and ensure reliable performance and cost efficiency.

Own and automate the end-to-end ML lifecycle: build, deploy, monitor, test, and retire models at scale. Design production-grade, highly available ML infrastructure and CI/CD pipelines, implement governance, and collaborate with engineering and security teams.

Build, operate, and scale cloud-native backend services and APIs for a high-volume vehicle marketplace. Embed AI-driven capabilities into features, use AI tools to accelerate development, participate in on-call rotations, improve reliability, and collaborate with Product, Data, Infrastructure, and SRE teams to deliver production-ready systems.

Lead technical engagements with robotics customers to design and integrate voice AI into mobile robots, own POCs and technical evaluations, clear deployment roadblocks, collaborate with sales/product/services, and feed customer learnings into the product. Travel across Europe for onsite evaluations (20–30%).

Lead integration of AI-driven capabilities into Land C4ISR systems: define architecture and system design, manage AI lifecycle and V&V for non-deterministic components, oversee integration testing (sensor fusion, M&S), advise senior management, prepare technical/procurement documentation, and evaluate AI algorithm performance and reliability in tactical environments.

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.

Provide end-to-end technical support for OpenAI products, troubleshooting API failures, integrations, authentication, and production incidents. Own escalations, reproduce issues, analyze logs, and collaborate with Engineering and Product to prevent recurrence. Drive automation and AI-powered tooling to scale operations, and provide occasional on-call holiday/weekend support.

Lead end-to-end AI transformation engagements: diagnose opportunities, design roadmaps, prototype and build 0-to-1 AI products, manage senior client relationships, deploy AI tooling, and support go-to-market and internal delivery practices.

Lead the end-to-end customer-facing conversational agent across chat and voice. Own roadmap, conversation design, partner integrations, and interface UX. Drive daily design in Figma, build prototypes with AI coding tools, define quality metrics (resolution, containment, escalation), and iterate from production data while coordinating GTM and partner teams.

Review, evaluate, and validate AI-generated content (text, audio, images, video). Annotate and label data, identify errors and quality issues, compare outputs for accuracy, provide written justifications, follow project guidelines, complete tasks independently on schedule, and attend training and office hours.

Administer and configure conversational AI, agent assist, workflow automation, and related platforms. Manage users, permissions, environments, releases, integrations (CRM, contact center, auth, knowledge), deployments, testing, and governance. Troubleshoot APIs/integrations, maintain documentation and change history, and support security, privacy, and compliance while working with analytics to optimize AI workflows.

The Chief Scientist will lead advancements in Physical AI by developing robotic foundation models and integrating feedback into scalable systems. Responsibilities include designing experiments, collaborating on research outputs, and ensuring system coherence across robotics applications.

Lead architecture and implementation of agentic AI systems powering Zuma's platform. Rebuild onboarding and integration frameworks, implement analytics and observability for LLM performance, translate product needs into technical solutions, mentor engineers, establish engineering standards, and work directly with customers to enable scalable, production-grade AI experiences.

Lead end-to-end development and production deployment of agentic AI systems and full-stack products. Architect integrations with property management systems, improve reliability and observability, define engineering standards, and partner with product and operations to ship scalable AI agents across voice, text, and email.