Top AI Jobs in Calgary

Build and support production AI applications, assistants, and agent workflows embedded with customer teams. Responsibilities include business discovery, full-stack development, LLM and RAG integration, enterprise API and data integration, AWS serverless deployment, testing, evaluation, monitoring, security, troubleshooting, documentation, user adoption, and production support. The role also involves collaborating with stakeholders, creating reusable components, reviewing developers’ work, and integrating with SAP and ERP systems.

Own the vision, roadmap, execution, and quality measurement for Clio’s AI Search platform. Lead engineering and ML teams developing hybrid lexical and semantic search across documents, emails, and records. Improve relevance through ranking, query understanding, embeddings, A/B testing, click-through analysis, and offline evaluations. Coordinate across Clio products while balancing relevance, latency, cost, permissions, and customer AI preferences. Translate customer search needs into measurable product improvements and guide both new product development and platform scaling.

Lead strategic product and growth partnerships with Big Tech, legal technology firms, ISVs, and other technology partners. Build and prioritize a high-value deal pipeline, negotiate complex agreements, collaborate with Product, Engineering, GTM, Legal, and Compliance, and develop partnership monetization strategies. Mentor business development and partnership teams while driving measurable revenue, lead generation, product alignment, and long-term partner value.

Leads claims audit, investigation, fraud risk management, prevention, and education programs for a health benefits organization. Develops multi-year strategies, oversees risk-based audits and investigations, strengthens controls, partners with providers, regulators, and law enforcement, and reports findings to senior leadership and Board committees. Uses analytics and artificial intelligence to identify risks and improve case selection. Manages and develops the investigation and claims audit team while ensuring fairness, confidentiality, compliance, and continuous improvement.

Lead complex enterprise customer engagements involving secure, resilient, AI-enabled Azure solutions. Advise executives and technical leaders, improve solution health, performance, and availability, resolve critical incidents, and drive cloud adoption and business value. Deliver proactive assessments, workshops, guidance, and support while collaborating with account, engineering, and support teams. Influence product roadmaps, identify growth opportunities, mentor technical talent, lead projects, and contribute to customer success strategies and on-call support.

Develop customer-specific AI solutions for consequential business decisions. Investigate internal and public information, evaluate evidence, identify uncertainty, and design reliable AI workflows. Work directly with customers to understand needs, prototype solutions, test them with users, and improve their usefulness. Translate successful workflows into repeatable product capabilities while partnering with engineers on integrations and missing technical functionality.

Lead technical customer engagements for Agentic AI and cloud application platforms. Design secure, scalable, resilient architectures; conduct workshops, demonstrations, whiteboarding, prototypes, proof-of-concepts, and pilot deployments. Advise technical and business stakeholders, influence Azure adoption and technical decisions, modernize application platforms, and deliver AI transformation outcomes. Collaborate with engineering, customer success, partners, and specialists while sharing reusable architectures and best practices.

Own daily operations and quality assurance for an internal AI-powered growth platform. Test releases, rule changes, edge cases, and regressions; document and verify bug fixes; monitor platform and agent activity; manage approval queues; evaluate agent output; escalate risks; and maintain test plans, QA checklists, runbooks, and release notes.

Build and ship production AI systems for enterprise portfolio and planning workflows. Develop anomaly and signal detection, LLM-powered insight synthesis, natural-language planning rule translation, evaluation frameworks, and MCP tools. Partner with external AI providers initially, then establish internal standards for prompt design, model selection, agent architecture, reliability, and evaluation. Collaborate closely with product, design, and domain engineering teams.

Own the strategy, roadmap, architecture, and adoption of the company’s central data infrastructure and BI platform. Partner with analytics, engineering, and business teams to establish trusted KPI reporting, semantic models, scalable warehousing, BI and ETL tooling, clear SLAs, and AI-powered self-service insights. Translate ambiguous needs into testable requirements, evaluate technical solutions, prototype quickly, and measure adoption, quality, and reporting efficiency.

Define the vision, strategy, and roadmap for AI-powered products. Lead use-case evaluation, requirements, experimentation, development, deployment, adoption, and continuous improvement across Generative AI, LLM, RAG, and agentic solutions. Collaborate with engineering, data science, design, and business stakeholders; establish success metrics; manage AI risks; and communicate product value, progress, and strategy to leadership and clients.

Leads the architecture, deployment, and scaling of secure, auditable agentic AI solutions. Partners with executives, clients, industry leaders, and engineering teams to identify use cases, translate business needs into production-ready systems, deploy solutions through AltaForge, establish evaluation standards, and deliver measurable adoption, ROI, and lifecycle value. The role also supports account growth and recurring revenue through successful AI implementation.

Own end-to-end analytics engineering for AI and developer experience data. Instrument telemetry, build governed production pipelines, define trusted metrics, and create executive-facing dashboards and analyses. Partner with data engineering, AI, and data science teams to improve data quality and connect raw signals to decision-making. Present clear recommendations on AI investment, developer productivity, and ROI while using AI coding agents to build and maintain pipelines, dashboards, and analyses.

Own end-to-end analytics pipelines for AI usage, developer experience, engineering productivity, and spend. Instrument telemetry, build governed ETL workflows, define trusted metrics, and create executive dashboards and visual narratives. Analyze results, recommend improvements to AI ROI and developer experience, and communicate methodology to senior stakeholders. Collaborate across data engineering, applied AI, and data science while using AI coding agents to build, test, and maintain production data systems.

Build and maintain premium mobile features, subscription and in-app purchase flows, paywalls, pricing experiences, and advertising functionality for Life360’s consumer app. Own feature quality, performance, reliability, experimentation, and incident response at 100M+ user scale. Collaborate with product, design, data science, and analytics teams; use AI coding agents extensively; contribute to architecture and code reviews; mentor engineers; and help evolve AI-native engineering practices.

Lead global R&D to define technical strategy and roadmaps, prioritize AI-driven modernization, manage engineering and test teams, oversee AI/ML model validation in regulated clinical environments, own R&D budgeting and vendor partnerships, ensure regulatory compliance, and drive operational excellence and ethical AI adoption to improve clinical trial outcomes.

Build and operate production-grade AI agents that automate workflows across internal teams. Design agent architectures with LangGraph and Amazon Bedrock AgentCore, integrate internal systems and third-party tools, and develop evaluation, observability, and guardrail frameworks. Partner with nontechnical teams to identify workflows, own solutions from prototype through production, measure impact, and drive adoption through documentation and training.

Design, build, deploy, and optimize scalable AI and machine learning systems using large datasets. Develop predictive models, recommendation systems, personalization engines, forecasting solutions, generative AI applications, and production ML pipelines. Collaborate with data, backend, and product teams to integrate models into applications, while monitoring performance, data quality, drift, reliability, and cost. Establish best practices for evaluation, experimentation, reproducibility, and deployment, and research emerging AI technologies.

Eight-month intern supporting enterprise data, analytics, AI, governance, automation, and digital transformation initiatives. Responsibilities may include building data platforms and pipelines, analyzing data, developing dashboards, supporting AI-enabled solutions, improving data quality, researching emerging technologies, and preparing business materials. Interns collaborate with technical and business stakeholders while gaining hands-on experience with cloud platforms, data engineering, analytics, governance, and AI adoption.

Design, build, and productionize LLM-powered systems (adaptive content, tutors, evaluators) and AI pipelines (RAG, embeddings, fine-tuning). Prototype and optimize models across modalities, define AI safety/evaluation/observability practices, and collaborate with product and engineering to measure learner outcomes and iterate.

Lead the architecture, development, and scaling of data pipelines and infrastructure for a customer-facing Data Lake platform. Build ETL and ingestion systems using GCP, Debezium, Pub/Sub, Dataflow, BigQuery, Python, Golang, and SQL. Establish engineering standards, deliver strategic projects, mentor engineers, influence cross-functional technical decisions, optimize data services, and shape the platform roadmap.

The Legal Technology and AI Attorney applies restructuring expertise to identify, evaluate, and implement AI-enabled tools, automation, knowledge resources, and technology-supported workflows. The role develops templates, playbooks, guidance, and practice-support systems; pilots emerging technologies; trains attorneys and staff; and collaborates with legal, technology, research, and administrative teams to improve efficiency, consistency, and scalability in restructuring matters.

Supports litigation teams by evaluating, configuring, and implementing AI and legal technology workflows. Develops prompts, AI-assisted research and drafting processes, litigation analytics solutions, governance frameworks, training materials, and best practices. Collaborates with attorneys, IT, Information Security, and Knowledge Management to improve document analysis, legal research, case preparation, and technology adoption.

Attorney supporting the intersection of technology, AI, and legal practice. Responsibilities include identifying AI and automation opportunities, evaluating and implementing legal technology tools, designing workflows, maintaining knowledge assets, analyzing legal work product, training attorneys and staff, and advising Tech & Innovation lawyers. The role requires at least four years of technology-focused legal experience, bar admission, strong communication skills, and an interest in workflow design, tool development, or process improvement.

Develop and deploy UiPath RPA solutions and generative AI applications using AWS Bedrock, LLMs, prompt engineering, fine-tuning, and RAG. Integrate AI with business applications and automation workflows, build secure APIs and cloud-native services, support data governance, and apply DoD responsible AI and RMF controls. The role also includes testing, documentation, architecture participation, production support, and mentoring junior developers.