Top AI Jobs in Calgary

Lead end-to-end design for AI-powered developer tools at GitLab. Create flows, wireframes, prototypes, and high-fidelity interfaces for AI agents, chat, and code-assist features. Define success metrics, iterate with PMs and engineers, establish AI-specific design patterns in the design system, mentor other designers, and contribute to product strategy and research-informed decisions.

Design, build, and deploy agentic AI tools and production-grade models and workflows. Own end-to-end deployment, monitoring, and incident response. Collaborate with AI/ML and infra teams to ensure scalable, observable systems and release AI products to internal and external users.

Lead and build the marketing operations and agentic AI layer: define production standards for AI agents, deliver production-grade agents for lead scoring/enrichment/prospection/attribution, own martech stack and vendor decisions, report business outcomes to execs and board, hire and run a marketing engineering team, and cut tool costs while ensuring measurable revenue impact.

Design, build, and deploy scalable AI/ML systems and pipelines. Develop reusable Python packages, RAG/LLM integrations, agentic workflows, evaluation/monitoring frameworks, optimized data queries, and MLOps practices while collaborating across cross-functional teams.

Design and build a tiered AI education library: perform needs analysis, curate third-party content, develop QuadReal-specific AI learning (eLearning, videos, job aids), administer the LMS and content platform, engage stakeholders, manage full project lifecycle, and update content to ensure responsible, scaled generative AI adoption.

Build, deploy, and maintain AI-powered agents, APIs, and applications for People operations on Snowflake. Integrate Workday/Notion, ensure security and data governance, design multi-model LLM reliability, and own production lifecycle including CI/CD, monitoring, and incident response while working with non-technical stakeholders.

The Chief of Staff will lead AI transformation initiatives, designing scalable processes, managing client engagements, and ensuring effective delivery and governance of AI solutions across the organization.

Lead discovery and design of agentic AI solutions, translate business problems into production-grade architectures, deploy and scale systems on AltaForge, define evaluation standards, ensure secure and auditable workflows, and support account growth and measurable ROI.

Evaluate AI-generated coding interactions end-to-end, judging usefulness, high-level correctness, engineering judgment, explanations, and reasoning. Provide clear, opinionated feedback, distinguish quality levels, and help define standards for excellent AI-assisted developer interactions.

Evaluate AI-generated coding interactions (Codex/Claude Code) for engineering judgment, usefulness, high-level correctness, explanations, and developer experience. Provide opinionated feedback, distinguish response quality levels, and help define best practices for AI-assisted developer workflows.

Contract senior engineer evaluates AI coding agent interactions (Codex, Claude Code) for engineering judgment, reasoning quality, usefulness, and alignment with strong developer practices; provides opinionated feedback and helps define high-quality interaction standards. ~10-20 hrs/week, $100-$200/hr, short-term through early May.

Own end-to-end product experiences across web and native mobile: set design vision, run user research, rapidly prototype (AI-enabled), write production frontend code, evolve design systems, mentor designers, and drive measurable product outcomes.

Lead technical strategy and architecture for customer-facing AI products and the underlying platform. Author architecture decision records, set guardrails for agentic workflows, define eval/observability/persistence standards, and mentor engineers. Drive platform vendor decisions with cost models, design large-scale distributed systems integrating AI and product stacks, and own responsible AI, security, and compliance for payroll and tax reporting.

Lead enterprise AI architecture strategy and roadmap; design scalable AI, data, and integration architectures; establish reusable frameworks, standards, and governance; drive platform and technology selection; ensure security, compliance, and responsible AI; partner with business leaders and mentor teams to deliver enterprise-scale AI solutions.

Support and enhance ADI's global compliance program aligned to enterprise risk and ISO 37301. Monitor regulatory developments, update policies, execute risk‑based monitoring and testing, manage investigations and remediation, support audits, develop training and communications, track metrics, and collaborate with Legal, HR, Finance, IT, and Operations to drive continuous improvement.

Lead and support execution of ADI's global compliance programs: risk‑based monitoring, policy management, testing, investigations, training, ISO 37301 alignment, audits, remediation, and cross‑functional stakeholder coordination.

The AI Engineer will design and deploy LLM applications, optimize workflows, manage RAG systems, and perform full-stack development involving Python and Java.

The Applied AI Engineer will design, develop, and deploy LLM-powered applications, manage full product lifecycles, and ensure system reliability while utilizing AI-first workflows and collaborating with diverse teams.

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.

Lead product vision, roadmap, and delivery for AI and analytics across the Intelex platform. Conduct customer discovery, define PRDs and success metrics, partner with engineering and design, manage end-to-end launches, instrument and monitor feature adoption and quality, enable go-to-market teams, and ensure AI governance, explainability, and compliance for enterprise EHSQ use cases.

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.

Embed with enterprise customers to design, build, and deploy production agentic AI workflows. Integrate platform with customer systems, handle security and compliance reviews, drive adoption and expansion, and feed product improvements based on field signal.

Embed with enterprise customers to deploy production agentic AI workflows: integrate data sources, identity, CRMs and APIs; handle security, SSO/SCIM, isolated VMs and compliance; drive adoption, instrument usage, and feed product improvements to scale deployments.

Design, build, and productionize LLM-powered systems (adaptive content, tutors, evaluators) and AI pipelines (RAG, embeddings, fine-tuning). Prototype and optimize models across modalities, define AI safety/evaluation/observability practices, and collaborate with product and engineering to measure learner outcomes and iterate.