Top AI Jobs in Ottawa

Design, build, and operate production-grade Generative and Agentic AI applications and services. Build backend services in Python, integrate LLM APIs and agent frameworks, implement RAG pipelines and vector search, ensure reliability/security/observability, deploy on major clouds, and mentor junior engineers.

The engineer will develop features for an AI workspace platform, deploy AI solutions in public sector environments, and interact with clients.

Design, build, and optimize high-performance AI infrastructure systems to support intelligent agents, ensuring reliability and scalability for millions of users.

Evaluate AI outputs, audit model behavior, apply and refine labeling guidelines, identify failure modes and trends, document findings, and collaborate with cross-functional teams to improve model evaluation and data quality.

Evaluate AI outputs, perform audit-based reviews, identify failure modes and trends, apply and refine guidelines, communicate findings, participate in calibrations and stakeholder syncs, and help improve evaluation frameworks for human-in-the-loop AI.

Audit and ensure quality of data labeling outputs, coach and onboard Data Labeling Associates, provide actionable feedback, monitor workflows, identify patterns and edge cases, and help improve guidelines and processes in human-in-the-loop AI workflows.

Audit and review data labeling outputs, provide actionable feedback, train and support labeling teams, monitor workflows, identify recurring issues and edge cases, participate in calibrations and stakeholder syncs, and help improve guidelines and processes in human-in-the-loop AI workflows.

Evaluate AI model outputs, perform audit-based reviews, identify failure modes and trends, apply and improve guidelines, document findings, and collaborate with leads and cross-functional teams to refine quality standards.

Evaluate AI model outputs, perform audit-style reviews to find patterns and failure modes, apply and refine labeling/evaluation guidelines, document findings, communicate insights to leads and stakeholders, and participate in team calibrations to improve model evaluation and data quality.

Audit and ensure quality of labeled data by reviewing DLA outputs, providing feedback and coaching, supporting onboarding and training, monitoring workflows, identifying recurring issues and edge cases, and contributing to guideline and process improvements in human-in-the-loop AI workflows.

Audit and improve data labeling quality in human-in-the-loop ML workflows. Coach and onboard labelers, provide actionable feedback, monitor queues, identify recurring issues, and help refine guidelines and processes.

Evaluate AI outputs and audit model behavior, apply and refine guidelines, identify failure modes and trends, document findings, and collaborate with leads and cross-functional teams to improve evaluation frameworks and data quality.

Evaluate AI model outputs, perform audit-based reviews, apply and refine guidelines, identify performance trends and failure modes, document findings, and collaborate with cross-functional teams to improve model evaluation and data quality.

Audit and ensure quality of data labeling outputs, coach and onboard annotators, enforce and improve guidelines, identify recurring issues and edge cases, escalate risks, and document feedback to support human-in-the-loop AI workflows.

Audit and ensure quality of data labeling outputs, coach and onboard annotators, apply and refine guidelines, monitor workflows and escalate issues, document findings, and support human-in-the-loop AI evaluation processes.

Design, build, and deploy production-grade AI-powered backend systems. Integrate LLMs, vector DBs, and task-specific ML models into scalable, low-latency architectures. Optimize performance, background processing, and testing; debug cross-layer issues; collaborate with product and frontend teams to ensure reliable, well-monitored production services.

The AI Product Manager will lead the development of AI-powered applications, collaborating with engineers and designers to align AI technologies with user needs and business objectives.

As an AI Product Manager, you will lead the product development for AI-driven features, collaborating with engineers and designers to optimize user experiences and ensure alignment with business objectives.

The AI/ML Engineer will design, develop, and optimize machine learning models for mobile applications, collaborating with teams to enhance user experiences and innovate in AI-driven solutions.

As an AI/ML Engineer, you'll develop and optimize machine learning models for mobile applications, collaborate with teams, and integrate AI-driven features to enhance user experience.

Lead budgeting, forecasting, and variance analysis while building AI-driven financial models, scenario planning, KPIs, and dashboards. Automate FP&A processes, deliver insights to senior leaders, and collaborate with cross-functional teams to improve forecast accuracy and strategic decision-making.

Design, implement, and optimize AI-driven solutions across the claims lifecycle (intake, investigation, adjudication). Build ML models for data extraction, fraud detection, reserve setting, subrogation, and adjudication decision engines. Collaborate with underwriting, fraud, legal, and IT to integrate models into operations and improve efficiency, accuracy, and compliance.

Drive data-informed capital allocation by building financial models (NPV, IRR), applying AI/ML for forecasting and scenario planning, creating build-vs-buy and prioritization frameworks, implementing decision-support systems, collaborating with cross-functional stakeholders, and monitoring post-investment performance to refine allocation models.

Lead modernization of month-end and year-end close processes using AI and automation. Prepare, review, and validate journal entries, accruals, and financial statements. Implement anomaly detection, automated reconciliations, variance analysis, and predictive insights. Collaborate with data teams to structure finance data for ML use cases and design audit-ready, scalable reporting workflows.

Design, implement, and optimize AI-powered solutions to support financial compliance and controls. Monitor GAAP/IFRS adherence, automate controls and reporting, support internal/external audits, and manage complex accounting areas like revenue recognition and lease accounting while collaborating with finance, audit, and technology teams.