Top AI Jobs in Calgary

Design and build a production multi-agent harness and MCP integrations that operate across ERPs, CRMs, and business tools. Own end-to-end production concerns (reliability, observability, permissions, safe workflow execution), scope the product, make architecture decisions, and ship features working closely with CTO, product, engineering, and design. Stay current with agent frameworks and use tools like Claude Code in development.

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.

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.

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.

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.

Design, implement, and monitor AI/ML models to improve underwriting risk assessment, pricing, and portfolio management. Translate underwriting rules into data models, integrate models into workflows, analyze datasets for risk patterns, build decision-support tools, ensure compliance and fairness, and collaborate with underwriting, actuarial, product, and engineering teams.

Design realistic, specialty-specific clinical case scenarios and gold-standard attending-level responses, evaluate and grade AI-generated outputs using structured rubrics, provide detailed written feedback to improve model clinical reasoning, and participate in onboarding, calibration, and review sessions in a fully remote, asynchronous contractor role (~20 hours/week).

Contract role combining actuarial science, risk management, and AI to improve portfolio performance. Responsibilities include loss and combined ratio analysis, portfolio risk assessment, catastrophe and exposure modeling, stress testing, and integrating ML to enhance pricing, reserving, and risk selection while collaborating with underwriting, finance, and product teams.

Design and implement AI/NLP solutions to automate insurance policy administration (issuance, endorsements, renewals). Translate policy language into models and rule engines, integrate with PAS, ensure regulatory compliance, monitor model performance, and support underwriting, operations, and exception handling.

Manage and optimize end-to-end account reconciliations, intercompany eliminations, and chart-of-accounts governance. Identify discrepancies, perform variance analysis, and implement AI-driven automation and anomaly detection to reduce manual effort and accelerate close. Collaborate with cross-functional teams to resolve mismatches and improve financial data quality and reporting accuracy.

Lead AI-enabled financial planning and strategic analysis, build data-backed business cases, refine unit economics, deliver executive-level insights, integrate ML into FP&A, and track KPIs to drive profitability and strategic decisions.

Build and maintain operating and cash-flow models, apply AI/ML and data techniques to automate forecasts, run sensitivity and scenario analysis, translate strategy into financial outcomes, support fundraising/M&A/pricing, and create dashboards and reports for stakeholders.

Lead integration of AI-driven tools into tax provision, planning, and multi-jurisdictional compliance. Design ML/analytics solutions for sales and payroll tax, enhance regulatory filings, support audits, and collaborate with finance, legal, and technology teams. Monitor tax regulation changes, document AI tax models for transparency and governance, and continuously refine models for scalability, reliability, and compliance.

The Senior AI Platform Engineer will build and scale an AI procurement platform by developing backend services, integrating AI workflows, and creating user-friendly interfaces.

Lead AI initiatives, establish strategy, and develop generative AI applications. Collaborate with teams to build and optimize financial products.

The AI Enablement Lead will design and conduct training programs to improve AI fluency within Juniper Square, focusing on tools like Claude and Cowork. Responsibilities include curriculum development, leading sessions, coaching, and measuring adoption outcomes.

The Sales Strategy and Operations Manager leads complex sales opportunities, develops ROI models, engages executives, and coordinates cross-functional input for compelling proposals.

Lead and mentor a distributed engineering team to design, build, and operate AI-enabled web products. Drive architecture, code quality, CI/CD, observability, and AI-assisted development workflows using modern full-stack technologies. Partner with Product and Design to deliver scalable APIs, cloud-native systems, and data/AI integrations while reducing technical debt and improving delivery predictability.

Design, build, and scale MLOps infrastructure and CI/CD/continuous training pipelines. Deploy and monitor ML models as microservices or batch jobs, manage cloud GPU/CPU resources with IaC, enable observability for model drift and latency, and collaborate with data scientists, engineers, and product teams to ensure reproducibility, compliance, and production reliability.

Lead Life360's Data Engineering organization and own end-to-end data platform and analytics engineering strategy. Define architecture for ingestion, transformation, modeling, and serving data for humans and LLMs. Manage engineering managers and teams, drive self-serve data capabilities, cost management, vendor decisions, data quality/governance, and partner cross-functionally to prioritize engineering work that drives business impact.

Develop AI-powered features, design APIs, integrate AI/ML models, and enhance developer experience for ClickHouse's platform.

Build and own agentic systems on ClickStack to investigate incidents: design reusable skills, implement MCP servers and SDKs, integrate telemetry, optimize latency/cost, run evals, and collaborate with OSS contributors and customers to make agents production-ready and observable.

Develop and optimize AI pipelines for workflow intelligence, experiment with models, and collaborate to integrate AI systems into workflows.

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