Top Azure Jobs in Calgary

Designs and maintains scalable cloud and on-premise infrastructure, Infrastructure as Code, Kubernetes environments, CI/CD pipelines, data streaming, caching, observability, and incident response systems. The role owns AWS reliability and cost efficiency, supports progressive deployments, troubleshoots complex production issues, partners with software teams, and drives automation and continuous improvement across platform operations.

Own end-to-end technical relationships for enterprise customers: lead onboarding and complex implementations across cloud data warehouses, drive adoption and expansion, deliver executive QBRs, build AI-powered automations and internal tooling, and enable agentic AI workflows to reduce engineering dependency and accelerate marketing outcomes.

Lead and manage large-scale, multi-pillar cloud transformation programs using SAFe. Own PI planning, program cadence, risk and dependency management, stakeholder communication, and coaching of Scrum Masters. Support backlog organization, change management, and Jira/Confluence administration to ensure value delivery.

Lead and facilitate Scrum ceremonies, coach teams on agile practices, coordinate migrations with application owners, manage risks and dependencies, maintain Jira artifacts and sprint reporting to enable predictable delivery.

Lead strategic cloud migration planning and stakeholder discussions; assess on-prem VMware environments; define target cloud architectures and governance; design cloud network and security (Palo Alto); oversee hands-on migration of VMs and complex workloads to ROSA/AWS/Azure/GCP; implement containerization and OpenShift migration strategies.

Lead technical pre-sales for enterprise contact-centre AI deals: perform discovery and architecture assessments, design solutions, run demos and POCs, author RFP/RFI technical responses, engage technical stakeholders, and ensure delivery readiness and competitive positioning across assigned region.

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.

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

Develop and maintain cardiovascular medical imaging software on cloud platforms. Design and implement GUI workflows, backend services, and post-processing algorithms. Collaborate with developers, architects, and QA to deliver scalable, testable, and performant SaaS solutions.

Support threat intelligence, incident analysis/response, penetration test coordination, vulnerability scanning and remediation tracking, security tool administration, dashboard maintenance, and adherence to security policies while learning and developing cybersecurity skills.

Define, govern, and maintain global security architecture spanning people, processes, data, and technology. Lead cloud security strategy for GCP and Azure migrations, embed security into CI/CD and containerization, develop guardrails for Generative AI/LLMs, conduct architecture reviews and assurance, implement security tools, produce technical design documentation, and ensure policies and controls align with risk tolerance and compliance frameworks.

Serve as the technical lead for strategic customers: onboard, train, troubleshoot, and drive adoption of Tailscale. Build relationships, conduct QBRs, recommend best practices, escalate product feedback, and maintain customer-facing documentation to enable long-term success and expansion.

Own and evolve cloud infrastructure across AWS/GCP/Azure, build CI/CD and containerized deployments, design multi-tenant environments, implement monitoring and IaC (Terraform/Ansible), lead data protection and threat detection, and support compliance and security audits.

Build and maintain large-scale, cloud-based detection and threat-intelligence systems. Design, implement, and ship high-throughput streaming data pipelines and production-grade APIs, mentor engineers, lead architecture and cross-functional initiatives, remove technical debt, and ensure operational excellence including on-call support and security best practices.

Build and productionize ML systems for personalization, search, and recommendations. Apply statistical and ML models using Python and SQL, collaborate with stakeholders, and measure business impact across cloud environments.

Owner of cloud infrastructure and reliability for a cloud-native platform: design and maintain Azure/Terraform infrastructure, manage Kubernetes/Helm deployments, build CI/CD pipelines and observability, operate PostgreSQL at scale, lead incident response and support migration from legacy .NET/MSSQL to modern services.

Hands-on IT Systems Engineer responsible for endpoint management, identity and access administration, cloud and network support, infrastructure-as-code, compliance (SOC2/FedRAMP/CMMC), automation, and documentation. Own device lifecycle, IdP provisioning, VPN/segmentation, Terraform IaC, audit responses, and cross-functional IT initiatives to scale secure corporate operations.

As a Machine Learning Ops Engineer, you'll develop robust code, build and maintain MLOps infrastructure, QA systems, and mentor junior engineers to enhance AI model performance for mining clients.

As a Forward Deployed ML Engineer, you will apply machine learning to mining operations, develop neural network architectures, and communicate findings to non-technical stakeholders while working independently.

Lead advanced analytics and ML projects: perform EDA, feature engineering, statistical modeling, and build/optimize production ML and Generative AI solutions (LLMs, embeddings, RAG). Collaborate with engineering and MLOps to deploy and monitor models, create visualizations and dashboards, translate insights for stakeholders, and mentor junior team members.

As a Backend Engineer at Trust Wallet, you'll develop backend features for multiple platforms, maintain infrastructure, ensure system monitoring, and foster team growth.

Design and advance core infrastructure for engineering, ensure Kubernetes reliability, automate operations, and support AI infrastructure.

Lead enterprise data, analytics, AI, automation, and information governance. Define strategy and roadmap, modernize data platforms (lakehouse/Azure Databricks), establish AI and automation capabilities, enforce governance/MDM (Semarchy), deliver BI/Power BI and predictive solutions, run Automation CoE, drive data literacy, and build a high-performing cross-functional team.

Lead presales engagements to design enterprise data platforms and governance on Microsoft Fabric and Azure Databricks. Conduct discovery workshops, architect governance with Purview/Unity Catalog, define AI-readiness and Copilot access, author proposals/RFP responses, run POCs/demos, and partner with sales and delivery teams to size and transition engagements to managed services.

Architect and design complex Azure cloud infrastructure, lead migrations and CAF workshops, create landing zones, implement DR/BC plans, enforce security/compliance, automate deployments, and guide development teams.