Top AI Jobs in Ottawa

This job is for candidates with AI infrastructure experience to submit their resumes for future opportunities. No specific role is posted.

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.

Lead global data and AI engineering to deliver scalable, secure, and compliant data platforms and MLOps capabilities. Own strategy, architecture, operations, SRE practices, observability, and reusable self-service components to support analytics, AI, and enterprise programs.

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.

Build and productionize AI systems for logistics: multi-agent copilots, forecasting/optimization models, evaluation and observability, continuous learning loops, and operational reliability. Mentor engineers and make build-vs-rule tradeoffs while shipping high-impact ML services.

Design end-to-end cloud-native AI and data platform architectures enabling Digital Twin and Knowledge Graph solutions. Lead architecture discussions, define scalable platform patterns, collaborate with AI/data/infrastructure teams, evaluate emerging AI technologies, produce technical documentation, and support solution discovery and roadmap planning.

The Principal AI Consultant advises on AI application in Financial Services, focusing on operational relevance and business alignment, leveraging deep industry knowledge to enhance customer experience and efficiency.

Support FP&A forecasting, budgeting, and rolling forecasts; build driver-based financial models; analyze variances and deliver insights; create BI dashboards; validate data across ERP, Anaplan, and Snowflake; and automate reporting while enabling AI-driven forecasting.

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.

Architect and build production AI-powered HR systems using agentic workflows, multi-agent systems, APIs, and workflow automation. Integrate Workday and other HR tools, implement governance, audit logging, privacy controls, and human approval gates, and ensure reliable payroll-adjacent workflows. Document solutions, train People team users, drive adoption, and measure automation impact. The role requires strong software engineering, AI application development, HR technology, Workday, data privacy, and workflow automation experience.

Design and build scalable AWS cloud, Big Data, and machine-learning infrastructure for AI-powered Workforce Management. Lead distributed data pipeline and workflow-orchestration projects from architecture through production, improve reliability, scalability, observability, security, and cost efficiency, and develop automated testing. Collaborate across engineering, data science, product, and platform teams while influencing architecture, owning major initiatives, reviewing designs and code, and mentoring junior engineers.

Build and ship AI agents and automation workflows for Customer Support, identify operational friction, and manage production quality and performance. Own the knowledge base, taxonomy, and customer self-service experience while analyzing support data to improve answers and identify product gaps. Lead a small team, partner cross-functionally with AI, Product, Marketing, and Support stakeholders, and report on efficiency, quality, retention, and conversion metrics.

Build and maintain high-throughput, low-latency backend services for Life360’s AdServer and Exchange platform. Own projects from architecture and implementation through deployment, improve real-time bidding and auction logic, integrate advertising partners, and support observability, incident response, and reliability. Collaborate with architects and product teams, use AI coding tools daily, mentor junior engineers, and contribute to engineering standards.

Design, build, and maintain automated test suites, evaluation scenarios, and CI/CD quality gates for AI-powered applications. Test APIs, validate ML model outputs, create measurable evaluation rubrics, mentor teams, and contribute to shared QA frameworks and codebases.

Design, prototype, and ship AI-powered observability features using LLMs and agentic workflows. Rapidly iterate with cross-functional teams to integrate AI into incident detection, triage, and automated analysis. Build scalable, production-ready solutions in cloud-native environments and use observability tooling to troubleshoot and improve system behavior. Own end-to-end delivery and collaborate on product, data, and infra integration.

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.

Build and scale AI data and ML infrastructure: evaluation systems, fine-tuning pipelines, agent-first product surfaces, high-throughput data workflows, and integrations of new models into production.

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.

Build and maintain internal AI enablement tools, workflows, and guardrails to help engineers use coding agents and LLMs safely. Evaluate models, partner with teams to apply AI, track adoption and impact, and provide documentation, coaching, and advocacy to increase engineering velocity.

Lead global R&D to define technical strategy and roadmaps, prioritize AI-driven modernization, manage engineering and test teams, oversee AI/ML model validation in regulated clinical environments, own R&D budgeting and vendor partnerships, ensure regulatory compliance, and drive operational excellence and ethical AI adoption to improve clinical trial outcomes.

Lead strategy and delivery of AI-powered capabilities across Employ's recruiting platforms. Define product vision, prioritize roadmap, partner with engineering, design, and data science, ensure responsible AI practices, measure product success, and drive adoption to improve recruiter productivity and customer outcomes.

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.