Top AI Jobs in Calgary

Join a VC-backed startup network connecting Director/VP-level AI/ML leaders to portfolio companies. Lead AI strategy, build and scale ML teams, deploy and improve production ML systems, set model quality and responsible AI standards, partner with product and engineering, oversee data and MLOps, evaluate models and tooling, and communicate strategy to executives and boards.

Join a talent network connecting senior/staff applied AI scientists to VC-backed startups. Responsibilities include researching and developing ML methods, fine-tuning foundation models, designing experiments and evaluation frameworks, building prototypes, collaborating with engineering/product to productionize models, improving model robustness and efficiency, curating datasets, investigating failures, mentoring peers, and communicating results to stakeholders.

Join a talent network connecting Principal AI/ML Engineers with VC-backed startups. Responsibilities include architecting and deploying production ML/DL models, researching state-of-the-art methods (LLMs, transformers, RL), building scalable training/inference pipelines, implementing robust MLOps, optimizing model performance, and leading technical strategy and teams.

Join a talent network connecting founding AI engineers with VC-backed startups. Responsibilities include designing, building, and deploying ML/DL models, creating scalable data pipelines, optimizing real-time and batch model deployments, developing RAG and LLM systems, and aligning AI strategy with founders and product goals.

Develop and maintain MCP tools and the MCP Toolbox for Databases; expand test coverage for agentic coding assistants; refactor and maintain test frameworks; convert technical posts into production-ready sample code and documentation; build real-world data applications (QueryData) to support product R&D and collaborate with cross-functional teams to accelerate agentic AI adoption.

The Data Scientist will develop data labeling strategies, optimize datasets for LLMs, analyze data quality, and collaborate with engineering on data processing pipelines.

Design, develop, fine-tune, and optimize generative deep learning models (GANs, VAEs, transformers). Collaborate with cross-functional teams to integrate and deploy AI solutions, troubleshoot model issues, document models, and communicate technical concepts to non-technical stakeholders.

Lead product marketing for Procore AI & Data products: own positioning, messaging, launches, competitive positioning, sales enablement, and cross-functional GTM programs. Collaborate with Product, Demand Gen, Sales, and global Marketing to create customer-facing content and drive adoption.

Lead hands-on engineering execution and people management for Thunderbolt, defining production readiness, release discipline, and engineering practices for an enterprise-ready open-source AI product across desktop, web, and mobile.

Lead enterprise data, analytics, AI, automation, and information governance. Define strategy and roadmap, modernize data platforms (lakehouse/Azure Databricks), establish AI and automation capabilities, enforce governance/MDM (Semarchy), deliver BI/Power BI and predictive solutions, run Automation CoE, drive data literacy, and build a high-performing cross-functional team.

Build and scale a full-stack Next.js application tracking PRs, CI health, test parity, and benchmarks across AI accelerator open-source projects. Integrate GitHub APIs and webhooks, design PR/metrics schemas, visualize results, and produce technical digests and reports on cross-accelerator parity and performance trends.

Lead AI risk assessments, reviews, and monitoring to support AI governance at a financial institution. Evaluate internal and third-party AI solutions, maintain an enterprise AI inventory, analyze controls and risk metrics, build dashboards and reports, and provide quantitative and advisory support across stakeholders to enable responsible AI adoption and regulatory readiness.

Lead product design for Stripe's Data & AI products, shaping systems and new products from ambiguous strategic problems to detailed interaction design. Collaborate cross-functionally with PMs, engineers, and design leaders to build trust-focused, end-to-end experiences and design systems.

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.

Senior individual contributor who architects, prototypes, and ships production-grade AI solutions for Finance. Focus areas include agentic AI, multi-agent orchestration, ML forecasting and anomaly detection, RAG and vector-store solutions, responsible AI and governance, and embedding AI into FP&A processes. Partners with engineering, IT, security, and finance teams to productionize systems and establish standards, monitoring, and controls.

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.

Own delivery of AI agent deployments for consumer brands from kickoff to sustained automation performance. Manage complex multi-stakeholder projects, diagnose CX ops issues (helpdesk configuration, routing, automation), monitor metrics, provide proactive updates, and co-lead strategic accounts while feeding operational patterns back to product.

Build and maintain consumer-facing front-end experiences (primary) and backend services (secondary) for post-purchase products. Implement UI components from Figma, own frontend architecture, mentor engineers, and integrate AI-first workflows. Contribute to Go/Node.js services, deploy on cloud (GCP/AWS/Azure), work with datastores (Postgres, Spanner, BigQuery, Bigtable), and implement REST/GraphQL APIs.

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.

This role involves driving new client acquisition and revenue growth through full-cycle sales execution and relationship building with stakeholders, while maintaining pipeline tracking in CRM.

Review sports video to identify sport type, player actions, tactical movements, and game events. Apply predefined taxonomies, record timestamps, ensure annotation consistency, follow guidelines, and participate in quality calibration and testing to train AI systems.

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.

Lead product strategy and execution for Autodesk's Flow AI platform in Media & Entertainment. Define AI-driven experiences, integrations (Autodesk and third-party tools), and natural-language extensibility. Drive cross-functional alignment, prioritize using customer and workflow research, and deliver adoption, productivity, and creative capability outcomes across film and game production pipelines.

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.