Top AI Jobs in Calgary

The Artificial Intelligence Engineer at Precision AI will lead AI/ML projects, develop and optimize machine learning models, and ensure technical quality while mentoring junior engineers.

Design, train, and deploy ML and NLP models (including custom transformers) using modern tooling; prepare and transform large datasets; monitor production pipelines; communicate results to stakeholders; ensure model reliability, explainability, and ethical alignment.

Build and maintain backend features for Agent Observability across the GitLab monolith and AI gateway. Design and implement GraphQL/REST APIs, improve automated tests (RSpec), collaborate cross-functionally, troubleshoot production issues on Tier 2 on-call, and help shape responsible AI integration and platform architecture.

Build scalable infrastructure for machine learning training, deployment, monitoring, retraining, and continual learning. Responsibilities include distributed training pipelines, dataset and annotation workflows, experiment tracking, model versioning, benchmarking, CI/CD, cloud-to-edge deployment, inference APIs, and embedded NVIDIA platform optimization. The role collaborates with computer vision, navigation, embedded software, and cloud engineering teams to move AI research into reliable production systems.

Build, prototype, and deploy production-quality AI agents, copilots, and automation workflows integrated with enterprise and OT systems. Own end-to-end technical delivery, testing, monitoring, and hand-off. Translate business requirements with AI Transformation Managers, evaluate AI platforms, build guardrails, troubleshoot scale issues, create reusable accelerators, and mentor junior technical staff.

Develop experimental AI techniques for agentic marketing applications, especially image and video generation. Build proofs of concept, autonomous evaluation and improvement systems, realistic AI-generated video, and brand-aligned creative generation workflows. The role requires strong quantitative and probabilistic thinking, backend architecture skills, creativity with LLM applications, and product intuition.

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.

Build and deploy production GenAI systems, agentic workflows, retrieval pipelines, and data services for enterprise customers. Learn operational domains firsthand, redesign business processes, create evaluation harnesses, write production Python or TypeScript, and deliver observable AWS-based systems. Partner with executives, engineers, and operators to drive adoption and measurable business outcomes while contributing reusable industry blueprints.

Own Aleph’s shared AI foundation, including model selection and routing, proxy infrastructure, context management, tool design, and evaluation systems. Ship production agentic features using prompt engineering, caching, parallel tool calls, and subagents. Build observability for agent performance, collaborate with customers and finance experts to define quality standards, and drive adoption of reliable AI systems across product teams.

Research and optimize AI and machine-learning systems across desktop and mobile platforms. Evaluate model performance, develop and deploy on-device applications and agents, and improve browser ML products. Apply systems and network expertise, collaborate with privacy and security researchers, and investigate AI risks. The role involves open-source development, performance benchmarking, and use of modern ML frameworks to deliver privacy-focused solutions.

Lead the data strategy and own end-to-end data pipelines for AI training, simulation, and deployment in safety-critical autonomous systems. Build data flywheels, integrate with large-scale GPU/TPU training infrastructure, ensure simulator fidelity, partner with safety and certification teams, mentor engineers, and track KPIs for pipeline health and model readiness.

Industry Expert who reviews and defends advanced AI, ML, distributed systems, and forward-deployed engineering projects. Responsibilities include challenging architecture decisions, chairing mock review boards and executive presentations, hosting one or two technical sessions monthly, and serving as the escalation point for instructors. The role requires production-scale engineering judgment, strong technical communication, and expertise in agentic systems, LLM evaluation, infrastructure, observability, model serving, RAG, or enterprise deployments.

Leads the identification, development, testing, and scaling of AI and automation solutions across internal delivery processes and future customer-facing use cases. Builds automated test suites, scripts repetitive workflows, pilots generative AI and agentic tools, creates reusable automation assets, tracks measurable value, and enables teams through training and knowledge sharing. Collaborates with project managers, product owners, process owners, testing communities, and delivery teams to ensure maintainable, responsible, and broadly adopted solutions.

Build and ship full-stack features for an AI agent platform, rapidly prototype and iterate with pilot customers, and work hands-on with LLMs, agents, voice AI, and modern AI coding tools. The role also expects product ownership, independent opinions, strong engineering fundamentals, comfort with ambiguity, and a demonstrated passion for building through projects, hackathons, or open source.

Deploys, integrates, and scales enterprise generative AI solutions in customer cloud environments. Responsibilities include architecting GCP and Vertex AI infrastructure, deploying and optimizing LLMs, building RAG data and vector search pipelines, managing GPU/TPU workloads on GKE, automating infrastructure with Terraform, and advising clients on AI safety, prompt engineering, hallucinations, and inference costs. The role also collaborates with research and platform teams and requires travel to client sites.

Lead product discovery and delivery for client engagements, partnering with stakeholders, designers, and engineers to define opportunities, prioritize backlogs, manage roadmaps, and deliver measurable outcomes. Serve as a trusted advisor to senior clients while guiding AI-enabled product development. Apply AI tools for productivity and demonstrate practical understanding of LLM systems, RAG, data grounding, hallucinations, latency, cost, and evaluation.

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.

Participants are needed for an AI technology research study, offering compensation up to $600 for ~1.5-2 hours of participation over one week, requiring skills in tech-related roles.

Build and lead Replicant’s company-wide AI enablement program. Develop MCP integrations, shared agents, agent identity management, enterprise tool connections, reusable AI workflows, and cost-visibility mechanisms. Partner with Sales, Delivery, Finance, People, Engineering, and other teams to identify opportunities, implement solutions, promote AI literacy, and develop internal champions. Optimize AI systems through model routing, prompt caching, better agent design, and secure infrastructure.

Build Replicant’s internal AI enablement program by developing reusable agent workflows, MCP integrations, agent identity management, enterprise SaaS connections, and cost visibility mechanisms. Partner with Sales, Delivery, Engineering, Finance, People, and other teams to identify opportunities, implement secure AI solutions, create shared templates and skills, build AI literacy, and develop internal champions. Optimize AI infrastructure through prompt caching, model routing, tool consolidation, and improved agent design.

Own multiple client product engagements from discovery through delivery, including AI agent development, greenfield product builds, legacy platform modernization, and AI-native enablement. Translate ambiguous needs into PRDs, roadmaps, success criteria, and sequenced work; partner with engineers on agent architecture, evaluations, and guardrails; manage stakeholders, scope, budgets, timelines, and ROI. Lead client presentations, workshops, steering committees, and internal delivery playbooks.

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.

Build and deploy AI/LLM-powered features and RAG pipelines for internet-scale threat intelligence. Prototype, fine-tune, evaluate, and optimize models; integrate AI into platform services; implement CI/CD, regression testing, and security guardrails; collaborate across product, frontend, and data teams to deliver real-time, low-latency AI-driven analytics and automation.

Own client operating rhythm for Creative Operations and Systems/Process Design clients: maintain stakeholder context, turn vague requests into clear actions, use AI-enabled workflows for prep and follow-up, produce client-ready summaries, maintain decision logs and trackers, and coordinate with internal teams to prevent scope drift and improve account knowledge reuse.

Manage multilingual data collection and Large Language Model evaluation projects, ensuring quality assurance and effective global team coordination.