Top AI Jobs in Calgary

Perform hands-on product and player analytics across onboarding, retention, monetization, progression and LiveOps. Build and QA ETL pipelines in an AWS analytics stack, write cost- and performance-aware SQL, support A/B testing, and create reusable AI-enabled analytical workflows and notebooks to inform product, marketing, and LiveOps decisions.

Design, implement, and own a reusable on-device ML inference platform for resource-constrained devices. Integrate inference into RTOS firmware, develop drivers and DMA paths, debug on real hardware, train and optimize models for microcontroller-class hardware, and ship features to production while raising team expertise and using AI-assisted tooling.

The Forward Deployment Engineer will deploy and optimize Generative AI solutions, architect scalable infrastructure, and implement data pipelines across multi-cloud environments while collaborating with cross-functional teams to maximize business value.

Lead development of agentic AI features and shared platform components for a multi-tenant SaaS finance platform. Design LLM orchestration, CI/CD, deployment pipelines, observability, scalable background processing, ingestion and search systems, and PostgreSQL optimization. Drive technical vision, mentor engineers, and deliver production-grade, secure, and observable AI-first solutions end-to-end.

The role involves developing and productionizing security detection models, collaborating with security research, and shaping engineering culture as a founding team member.

Design, build, deploy, and optimize AI/ML models and pipelines (including LLMs and RAG). Integrate models into production, work with cross-functional teams, ensure monitoring and compliance, and contribute to scalable AI systems and automation.

Lead engineering to shift GTM from no-code automation to code-first, AI-agent-driven systems. Design scalable backend pipelines, integrate CRMs/APIs, build reusable agent frameworks, ensure reliability and observability, and hire/mentor a distributed engineering team to deliver production GTM infrastructure.

Design, build, fine-tune, and deploy production-grade AI and ML systems (LLMs, RAG). Develop scalable APIs, integrate AI into business systems, monitor model performance, and collaborate with cross-functional teams.

Lead high-volume outbound prospecting, run discovery calls, qualify and manage high-ticket opportunities through the full sales cycle, book and conduct meetings, handle objections, collaborate with founders/sales leadership, and consistently meet outreach, meeting, and revenue KPIs.

The Senior AI Solutions Engineer will architect and deploy AI systems on Google Cloud, collaborating with clients and delivery teams to create enterprise-ready solutions.

Build and deploy high-impact AI-native products, architect agent-driven workflows, and collaborate with teams to integrate cutting-edge AI solutions into production systems.

As AI Resilience Fund Manager, you'll establish and manage a fund focused on resilient investments, lead fundraising, and advise investors on navigating challenges created by AI and economic shifts.

The VP of Data & AI Engineering leads Duck Creek's enterprise data and AI strategy, overseeing global teams, implementing scalable platforms, and ensuring data governance and responsible AI practices.

Own and evolve ClickHouse's Python connectors and SDKs; build integrations with orchestration and transformation tools; design AI/LLM connector patterns for RAG, vector search, and feature pipelines; engage with open-source community; collaborate across product and engineering to prioritize and ship production-grade data integrations.

The Senior Software Engineer will develop AI/ML features, build robust APIs, create user-friendly UIs, and integrate AI models into ClickHouse Cloud.

Remote contract role applying equity research expertise to train and evaluate AI models. Review AI-generated financial analyses, analyze SEC filings and financial statements, create evaluation tasks and structured datasets, and collaborate with AI researchers to improve financial reasoning and assessment criteria. Minimum 2 years equity research experience; 10 hours/week minimum commitment.

12-16 week internship implementing and optimizing GPU/accelerator kernels and custom operators, profiling and improving LLM inference and runtime performance, building benchmarks, fixing regressions, and shipping code to production or open-source AI infra projects.

Research, design, and train foundation models for robotics; align text-based reasoning with low-level control policies; evaluate zero-shot generalization; run experiments; integrate models into robotic systems and present findings externally.

Develop multimodal intent-recognition models from gaze, gesture, and voice; implement socially aware navigation respecting personal space; design robot visual and auditory UI feedback; run user studies and rapidly prototype AI-driven voice and visual interfaces.

Design and architect multi-agent orchestration frameworks, build tool-calling pipelines and reasoning loops, and optimize latency and token usage for real-time agentic workflows and autonomous operations.

Run adversarial tests on language and multimodal models, build guardrails and real-time filters for autonomous tool use, and support RLHF alignment and constitutional AI development to ensure safe AI deployment.

Independent full-cycle recruiter managing sourcing, client engagement, offer negotiation, and placements using AI-driven tools. Acts as commercial advisor, provides market insights, and maintains self-directed billing and activity in a results-only, remote environment.

Lead product strategy and roadmap for AI and robotics products, translating VLA and agentic RAG breakthroughs into customer value, defining safety/usability/performance metrics, working with enterprise adopters, and driving cross-functional delivery.

Design and integrate vision and audio models into agent reasoning loops, optimize streaming latency for voice-to-voice interactions, and architect multimodal RAG systems to retrieve insights from videos and PDFs.

Optimize, compress, and distill large language and vision models for on-device inference. Build pipelines for distillation and hardware-specific compilation, and benchmark performance across NPU/GPU architectures.