Top AI Jobs in Calgary

Design and build AI-driven interactive prototypes and production-ready flows using AI coding tools. Own prompt-to-pipeline solutions, validate via experiments and user research, produce high-fidelity UX in Figma, and collaborate with engineering to move prototypes to production while aligning business, design, and tech.

Lead AI feature design and delivery, build large-scale ML systems, mentor engineers, and define best practices across the engineering organization.

Lead live sessions, office hours, and project reviews for senior engineers transitioning into system architecture. Provide detailed architecture critiques, mentor students one-on-one, run architecture review boards, record instructional videos, conduct mock interviews, and coordinate supporting instructors to ensure high technical standards and student readiness for architecture roles.

Author and refine a systems-engineering curriculum for experienced engineers: shape syllabus, define learning objectives, produce and fact-check AI-assisted lessons, design hands-on projects, write rubrics and reviewer guides, and iterate with an LX designer. Part-time (15-20 hrs/week) for ~4-6 months.

Build and ship LLM- and agent-powered product experiences for North, designing interfaces, evals, observability, and feedback loops to improve reliability and user outcomes. Collaborate with modelling, product, design, and customers to turn failures and feedback into measurable product and model improvements.

Execute hardware bring-up, validation, and telemetry monitoring for Cerebras AI clusters in data centers. Perform power-on sequencing, first-line troubleshooting, log collection, incident support under senior guidance, and contribute feedback to tooling and documentation while learning system architecture and networking fundamentals.

Integrate, validate, and productionize cross-stack inference features across AI frameworks, runtime, compiler, kernels, distributed systems, and hardware. Drive zero-to-one projects, debug system-wide failures, manage accelerated timelines, and improve automation, diagnostics, and repeatable integration practices while collaborating across software and hardware teams.

Design, build, and optimize distributed PyTorch training systems for multi-node GPU clusters. Profile and eliminate performance bottlenecks across data pipelines to GPU kernels, implement low-level CUDA/cuDNN/Triton kernels, tune CPU/GPU/memory/network utilization, and develop monitoring and debugging tools for large-scale training runs.

As a Senior Agentic AI Engineer, you will lead innovation in AI initiatives, from ideation to prototyping, collaborating with teams to deliver impactful solutions.

Lead development of back-end systems and end-to-end data/ML pipelines, build and deploy generative AI workflows, design and consume performant APIs, contribute to AI/ML architecture, mentor engineers, and communicate progress to leadership.

Develop and maintain backend services and data pipelines to support AI/ML and GenAI workflows. Build and consume secure, scalable APIs, work with microservices on Kubernetes, collaborate on design and code review, and grow applied ML skills under senior mentorship.

Build, train, evaluate, and deploy production computer vision models (detection, segmentation, OCR, barcode decoding, visual search). Own dataset quality, tooling, model releases, inference exports, and monitoring. Collaborate cross-functionally to improve model performance, robustness, and deployment for retail robotics and edge environments.

Lead Conversational AI Developer responsible for developing chatbots and virtual assistants, integrating AI technologies, and improving user interactions through data analysis and software development.

Design and implement advanced Generative AI models, optimize performance for LLMs, and collaborate with cross-functional teams to innovate AI solutions within the automotive sector.

As a Backend/AI Systems Engineer, you will develop backend systems for AI workflows and enhance core functionalities for Spellbook's legal software. You'll tackle complex problems, ensure system reliability, and collaborate with product teams, while maintaining a hands-on ownership approach.

As a Senior Full-Stack Software Engineer, you will design, build, and ship AI-powered systems to optimize workflows in the single family rental market, focusing on backend services and full-stack development in a fast-paced environment.

The Founding AI Engineer will design and build an AI-native compliance platform, handling complex financial data and developing scalable backend services, while establishing processes for enterprise readiness.

The AI Platform Engineer leads the development of production AI agents and components, collaborates on AI implementations, and maintains AI systems for reliability and observability.

As a Machine Learning Engineer, you'll develop scalable ML solutions using Google Cloud Platform and Vertex AI, collaborate with teams, and operationalize models from ingestion to monitoring.

Lead operational rhythms and client delivery, focusing on AI solutions. Collaborate with executives and manage multi-stakeholder projects for positive outcomes.

Lead enterprise platform and AI sales across healthcare segments, own end-to-end sales cycle and quota, build pipeline and C-level relationships, negotiate complex agreements, collaborate with Solution Engineering and Product Marketing, surface product feedback, improve buyer journeys, and ensure smooth handoffs to delivery teams.

Lead AI validation strategy for LLMs, RAG, and agent systems. Build automated CI/CD validation pipelines, design semantic and prompt regression tests, implement guardrails and confidence scoring, monitor production AI quality, and partner with engineering and data governance to ensure safe, accurate, production-ready AI.

Lead architecture, design, and delivery of enterprise-scale Agentic AI solutions. Oversee multiple engineering pods, mentor teams, manage client-facing architecture discussions, ensure production-grade MLOps, cloud infrastructure, microservices, observability, and adoption of emerging AI technologies.

Build and prototype state-of-the-art large language models and AI assistants, take services from ideation to production, fine-tune open-source models, use LLM platforms, analyze model statistics and projections, and collaborate cross-functionally in a fast-paced hybrid Palo Alto environment.

Lead the design and delivery of an end-to-end AI system layer across the product: agentic workflows, orchestration, evaluation, guardrails, observability, and developer platform. Build and grow a multi-team org, partner with Product and Design, define how AI decisions flow across cross-product context, and ensure reliable, measurable, and safe autonomous behaviors that proactively help small-business users.