Top AI Jobs in Calgary

The Senior AI/ML Engineer will design, develop, and deploy language models for African languages, focusing on NLP applications and cultural context.

Lead 0→1 product development for Emerald Cloud Services: define vision, conduct customer discovery, set product requirements for AI compute provisioning and management, partner with engineering and GTM teams, and launch and scale cloud services for enterprise and AI customers.

As an Applied AI Engineer at Luminai, you will design experiments, optimize machine learning models, and enhance AI platforms for healthcare automation.

As an AI Product Engineer, you will develop and deploy AI-driven software solutions, collaborate with customers, and optimize applications for real-world impacts.

The AI/ML Engineer will design and deploy advanced AI models, optimize performance, and directly engage with government officials to transform industries through innovative AI solutions.

As a Python and Kubernetes Software Engineer, you will develop data analytics solutions using open source tools, collaborating with a distributed team.

Embed with customers to learn their workflows, redesign and build production GenAI/LLM systems on AWS, ship and operate code, evaluate non-deterministic models, and own business outcomes.

Participants will learn to design intelligent AI workflows and automation tools using modern AI technologies and publish their own projects within eight weeks.

Lead enterprise Data & AI transformation initiatives, managing the delivery of cloud-based analytics platforms while aligning business and technology objectives. Responsibilities include program governance, stakeholder management, and overseeing implementations of Microsoft Fabric.

The Sales Strategy and Operations Manager leads complex sales opportunities, develops ROI models, engages executives, and coordinates cross-functional input for compelling proposals.

The Senior .Net Developer will enhance SDG's C#/.NET Core codebase, design high-load services, and optimize MS SQL queries, benefiting from a remote work environment and attractive benefits.

Build and scale production AI systems, APIs, data pipelines, retrieval systems, and model-serving infrastructure for consumer and clinician-facing health products. Integrate and fine-tune LLMs and other models, manage AI feature lifecycles from prototyping through monitoring, and ensure reliability and observability. Collaborate with engineering, product, and science teams. More senior candidates may lead AI architecture, roadmap development, and mentoring.

Own the generation side of Document Intelligence by designing prompts, schemas, validators, multimodal context assembly, and model-selection strategies. Build evaluation datasets and rubrics, monitor factuality and quality, identify regressions, and ship reliable LLM features into production. Work with video, PDF, audio, and image inputs to generate validated maintenance entities while balancing quality, cost, and latency.

Build and deploy AI-powered automations across the business. Partner with teams to identify opportunities, redesign processes, integrate systems through APIs, and launch scalable workflows using LLMs, agents, and automation platforms. Document solutions, establish safeguards, enable adoption, and measure efficiency gains. The role requires strong ownership, responsible AI practices, business understanding, and hands-on experience with AI development, APIs, Python or similar languages, SQL, databases, and modern deployment tools.

Develop backend systems for 3D AutoReview, including geometry parsing, model analysis, quality detection, rule systems, scalable APIs, and AI-assisted review workflows. Collaborate with backend, frontend, product, and machine learning teams to deliver production features. Build extensible systems for custom 3D rules and reference data while experimenting with CAD, machine learning, and large language model technologies.

Lead applied research on continual learning, fast adaptation, memory, and transfer learning for agentic systems. Own end-to-end pipeline from data generation to evaluation and product integration, run experiments, produce code, and drive research strategy while mentoring and managing a small team.

This job is for candidates with AI infrastructure experience to submit their resumes for future opportunities. No specific role is posted.

As a Software Engineer in AI Infrastructure, you will design and develop core platform components, build APIs and services, enhance performance, and automate tooling while collaborating across teams and improving system reliability.

Design and optimize high-performance kernels and custom operators for attention, MoE, GEMM, quantization and collective communication across NVIDIA, AMD and AWS Trainium. Improve LLM inference runtimes, develop distributed training/inference solutions at scale, use compilers and SDKs (Neuron, Torch Dynamo, PyTorch/XLA), contribute to open-source, and publish technical findings.

Lead end-to-end delivery of enterprise AI engagements: run discovery, size value, define features, coordinate cross-functional teams, drive adoption and change management, and own commercial expansion and program outcomes in regulated environments.

Part-time AI Application Engineer focused on measuring and improving healthcare AI agent performance in production. Responsibilities include instrumenting agent quality, identifying failure points, designing experiments, analyzing results, and partnering with clinical experts to improve patient outcomes. The role requires Python, strong analytical judgment, comfort with ambiguity, and interest in healthcare. Clinical experience is valued but not required. Engagement is structured as a paid internship or contract role with weekly Bay Area office syncs.

Lead executive interviews, stakeholder workshops, and current-state assessments to identify operational gaps and opportunities. Develop business cases, operating models, governance, KPIs, roadmaps, and practical AI-enabled solutions. Advise on AI strategy, innovation, commercialization, customer experience, and change management. Support transition of recommendations to delivery teams and occasionally participate in business development.

Embed with client engineering teams to deploy, integrate, and scale enterprise generative AI solutions. Build agentic systems, train and serve LLMs with Vertex AI, integrate models with BigQuery/AlloyDB/Spanner, implement streaming inference pipelines, and ensure operational reliability while collaborating with stakeholders to translate business goals into production-grade architectures.

Build high-performance API services and production-grade Generative AI systems. Design and implement end-to-end RAG pipelines, vector stores, embeddings, and latency-optimized generators. Develop agentic AI using LangChain/CrewAI/Semantic Kernel, integrate LLM APIs and AWS Bedrock, manage CI/CD and ML pipelines, and apply evaluation frameworks to detect hallucinations and optimize performance.

The AI Engineer designs, develops, and deploys AI-driven applications for non-data scientists, enhancing decision-making and model accessibility for organizations.