Top AI Jobs in Calgary

Lead end-to-end UX across Babylist web and mobile: run user research, build rapid interactive prototypes, write production front-end code, evolve the design system, instrument outcomes, and champion AI-native design workflows to create intuitive, delightful experiences for families.

Conduct original research on reliable agentic AI, developing methods for simulation, evaluation, optimization, regression control, and continuous learning. Analyze agent failures, traces, and human feedback; create experiments and prototypes; and translate research into production-facing systems. Collaborate with product and engineering teams, contribute to technical strategy, and provide rigorous experimental evidence. The role requires strong research depth, publication or open-source contributions, hands-on Python development, and expertise in LLM agents or agentic systems.

Own the company’s AI-powered quality and release infrastructure as its first dedicated hire. Build agent-run test harnesses, LLM graders, regression coverage, release gates, fresh-install and upgrade verification, and natural-language UAT. Manage CI health, artifact publication, deployment pipelines, credentials, certificates, and staging environments. Write production Python code, convert escaped bugs into regression tests, and provide trusted release-readiness evidence through dashboards, runbooks, retrospectives, and operating policies.

Build and maintain backend AI capabilities for GitLab Duo Chat: design flow components and agentic flows in Python/LangGraph, integrate LLMs and multi-agent orchestration, implement and maintain APIs in the Rails monolith, improve observability and reliability, and participate in on-call rotations and cross-functional collaboration to deliver safe, scalable AI chat features.

Own and improve the AI Social generative media pipeline: build and ship LLM-driven prompts/agent loops, choose and compose media (image, video, voice, music), collaborate with product/design/content, and deliver polished, production-quality social assets.

Lead Strata from inception as co-founder, defining the vision for regulatory intelligence software, engaging enterprise customers, shaping the product roadmap, providing technical leadership for trusted AI agents, and recruiting teams across go-to-market, product, engineering, and customer success. The role requires early-stage B2B SaaS experience, hands-on LLM and agentic architecture expertise, and the ability to build products emphasizing accuracy, citations, auditability, and expert review.

As a Staff Machine Learning Engineer, you'll lead AI initiatives using large-scale data, optimize ML models for edge devices, and collaborate with cross-functional teams.

Build and deploy internal AI tooling, agents, integrations, and workflows that improve productivity across engineering, research, and business teams. Evaluate emerging AI tools and models, develop observability and responsible-use guardrails, and partner with stakeholders to prototype and scale solutions. Drive adoption through documentation, enablement, and hands-on support while integrating AI into autonomy, simulation, ML training, and business workflows.

Serve as the technical sales and customer-facing expert for an AI platform. Responsibilities include conducting product demonstrations, supporting proofs of concept, advising on architecture and development, planning professional services projects, tuning retrieval and agentic pipelines, responding to RFPs, supporting customer adoption, and communicating product feedback internally. The role requires strong AI, cloud, on-premise, application development, storytelling, and enterprise customer engagement experience.

Provides technical leadership for agentic AI, ontology, knowledge graph, semantic, and enterprise data platform architecture. Designs reusable data models, ingestion pipelines, integrations, entity resolution, schema alignment, validation, and agent workflows across supply chain systems. Evaluates emerging technologies, establishes architecture and governance standards, mentors teams, and translates research into scalable product capabilities.

Build LanceDB’s revenue marketing function and enterprise demand engine from the ground up. Own account-based marketing strategy, segmentation, campaigns, content, account planning, martech implementation, pipeline reporting, and partner co-marketing. Identify high-intent accounts through open-source and product-usage signals, create tailored omnichannel programs, and collaborate closely with founders, Sales, RevOps, and Partners to source and influence enterprise pipeline.

Lead AI architecture across multiple client engagements, shaping solutions during pre-sales and governing designs through delivery. Define model hosting, security, data residency, integration, evaluation, observability, and cost standards. Maintain reference architectures and reusable accelerators, review code and designs, prototype solutions, mentor AI and data engineers, support technical hiring, and represent the company before enterprise architecture and security boards. The role requires hands-on enterprise software and production LLM experience, commercial judgment, and willingness to travel to Canadian client sites.

Partners with AI labs to integrate models, run evaluations, deliver results, and manage customer relationships. Builds pragmatic full-stack solutions, automations, onboarding workflows, and delivery tooling for cutting-edge AI teams. Translates customer and research feedback into product and engineering initiatives, supports strategic account growth, and serves as a technical point of contact across onboarding, releases, and issue resolution.

Own end-to-end AI product development for enterprise clients, from requirements and rapid LLM prototyping through production deployment. Responsibilities include data modeling, ETL pipelines, integrations, evaluation, observability, security, reliability, cost management, and client communication. The role requires shipping real AI products, iterating based on usage, and independently managing ambiguous technical and product decisions.

Build and scale AI-agent infrastructure, including orchestration, tool calling, context management, guardrails, evaluations, and LLM-as-judge pipelines. Develop Python and Node.js microservices, Laravel APIs, AWS infrastructure, and optimized MySQL systems. Diagnose agent failures, improve prompts and tools, maintain reliability and observability, contribute to architecture, review code, collaborate cross-functionally, and mentor engineers.

Partner with executives to understand business operations, identify and prioritize high-impact AI opportunities, define ROI, and create actionable roadmaps. Collaborate with engineers to scope solutions, inform architecture, maintain alignment with business outcomes, present recommendations to C-suite stakeholders, and manage handoffs to implementation.

Own paid acquisition across Google, Bing, Meta, and YouTube, scaling campaigns from creative production through launch, optimization, and attribution. Produce high-volume ad assets using AI tools, manage full-funnel campaigns, improve campaign processes, automate repetitive work, and analyze CAC, ROAS, payback, and product activation data. The role requires hands-on Google Ads expertise, creative production skills, subscription funnel experience, and strong ownership in a remote startup environment.

Leads complex government AI deployments from pre-sale discovery through production adoption, overseeing client relationships, delivery planning, cross-functional execution, change management, and executive communication. Translates agency workflows into technical requirements, manages risks and dependencies, drives measurable outcomes, and converts deployment feedback into product improvements. Builds deployment playbooks, operating cadences, and future delivery teams while supporting renewals and expansions. Requires strong AI/ML and enterprise systems fluency, senior stakeholder leadership, and 30–50% travel.

Supports responsible AI governance by developing policies, maintaining control frameworks, conducting AI risk assessments, monitoring regulatory compliance, preparing reports, and advising legal, compliance, security, AI, and business stakeholders. The role also tracks governance metrics, supports audits, researches emerging regulations, and recommends improvements to AI governance programs.

Design, develop, deploy, and optimize generative AI applications using LLMs and AI frameworks. Build scalable APIs, integrate foundation models and third-party platforms, automate business workflows, and connect AI solutions to internal systems and data sources. Monitor model quality, improve prompts and configurations, support responsible AI governance and security, evaluate emerging technologies, and collaborate with product, engineering, and business teams.

Designs, implements, and optimizes AI-powered workflow automation solutions. Responsibilities include analyzing business processes, documenting current and future-state workflows, identifying automation opportunities, gathering requirements, integrating automation with business systems, supporting testing and deployment, monitoring performance metrics, troubleshooting issues, training users, and promoting automation best practices and continuous improvement.

Leads the architecture, development, optimization, deployment, and lifecycle management of enterprise machine learning systems. Establishes AI engineering standards, MLOps practices, data quality processes, model governance, security, and validation frameworks. Provides technical leadership, mentors engineers, evaluates emerging technologies, and supports AI transformation initiatives while partnering with data scientists, engineering, product, and business teams.

Develop, optimize, test, and maintain prompts for conversational AI, automation, and business applications. Evaluate AI outputs, conduct prompt experiments, improve response quality, maintain prompt libraries, document standards, and support governance. Collaborate with AI engineers, trainers, product teams, and business stakeholders to translate requirements into prompting strategies and support Generative AI implementations. Research emerging techniques, evaluate tools, and recommend improvements to AI quality and effectiveness.

Leads AI data annotation operations, managing workflows, resources, productivity, quality assurance, and project timelines. Coaches and develops annotation teams, supports recruitment, and collaborates with AI engineers, trainers, and product teams. Establishes annotation guidelines, conducts audits, resolves quality issues, and drives process improvements, automation, tool adoption, and operational excellence across AI data initiatives.

Lead the organization’s AI workflow automation strategy, identify and prioritize automation opportunities, design scalable AI-powered workflows, and manage initiatives from concept through implementation. Partner with business and technical stakeholders, track budgets and timelines, establish governance, measure ROI and operational outcomes, develop performance dashboards, evaluate emerging technologies, and drive continuous improvement, adoption, and organizational transformation.