Top AI Jobs in Calgary

The AI Architect will design enterprise-grade AI solutions using Generative AI, LLMs, and orchestration frameworks, focusing on building scalable systems and developing production-ready applications.

Build and ship AI-driven GTM systems: outbound lead engine, inbound/lifecycle automations, AI-assisted content pipeline, data/reporting layer, and fast production patches to eliminate manual marketing workflows.

Design and implement advanced generative AI models, optimize deployment in production, collaborate with teams, and stay updated on AI advancements.

Close new business for GEO/AI search offerings across a territory by owning the full sales cycle. Build outbound-sourced pipeline, run consultative multi-stakeholder processes with senior marketing leaders, forecast accurately in CRM, and partner with Customer Success, Marketing, and Product to drive adoption and upsells.

Build and operate high-throughput experimentation and recommendation infrastructure. Design APIs and services, use agentic AI workflows (LLMs) for development, ensure performance and reliability, participate in on-call, mentor teammates, and codify AI-native engineering practices.

The role involves managing large-scale AI infrastructure projects, focusing on deployments, risk management, and cross-functional coordination while ensuring alignment with business objectives.

Drive the expansion of software development and AI services by managing client relationships, building sales pipelines, and negotiating contracts.

The engineer supports simple data hall designs, drafts AutoCAD drawings, assists with capacity planning, and learns data center principles under supervision.

Lead large-scale AI infrastructure deployment programs, manage project execution for GPU clusters and data center infrastructure, and mentor junior project managers.

Design, build, and maintain scalable AI/ML pipelines for data processing, model training, and deployment. Collaborate with data scientists and engineers to implement and optimize AI solutions, troubleshoot pipeline issues, document best practices, and develop MVP code to transition into production-ready systems.

Own end-to-end month‑end close (5-day target), maintain GL and reconciliations, run AP and vendor relationships, automate accounting workflows using AI/LLMs and Python, ensure audit-ready results, support audits and FP&A, and build integrations against NetSuite and financial systems to compress the close.

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.

The Artificial Intelligence Scientist will drive AI research and development for agricultural applications, focusing on creating innovative models to address challenges in crop monitoring and sustainability. Responsibilities include leading AI projects, developing models, and collaborating with cross-functional teams to translate research into practical solutions.

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.

Design, build, and operate CI/CD, Kubernetes-based platforms, deployment automation, and observability for engineering workflows. Improve reliability, performance, and scalability across cloud and on-prem environments, debug cross-boundary failures, perform root-cause analysis, and deliver durable platform software and self-service tooling.

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.

Design, implement, and maintain Python frameworks and services that orchestrate distributed engineering workflows across machines and clusters. Build scheduling, execution, resource management, failure recovery, and test infrastructure. Define APIs and abstractions, reason about concurrency and distributed-systems behavior, debug complex multi-system issues, write automated tests and documentation, and partner with platform, CI, release, QA, and product teams to deliver scalable infrastructure.

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.

Early-career SDET on Release Integration Testing for Cerebras AI Inference Core. Write automation and tests across the AI stack, triage cross-stack failures, maintain branch stability, collect qualification evidence, and collaborate with feature and infra teams to prepare features for production release.

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.

Lead Cohere's policy engagement on compute infrastructure, export controls, sovereign AI, and national AI competitiveness. Monitor and analyze relevant developments, develop policy positions, engage governments and cross-functional teams, represent Cohere in public discussions, and produce policy analysis and thought leadership to inform strategic business opportunities and public-private partnerships.

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