Top Azure Jobs in Calgary

Lead multi-year strategy, product architecture, and monetization for MongoDB's distributed query engine. Drive cross-functional alignment, mentor PMs, engage senior customers, own business cases, and represent the product externally to shape roadmap and market adoption.

Lead architecture and hands-on development for a zero-to-one, real-time content platform. Build scalable backend services, collaborative editing and synchronization systems, frontend visual editing experiences, APIs, publishing workflows, and developer extension layers. Partner with product and design, establish engineering standards, make build-versus-buy decisions, mentor engineers, and engage customers to validate architectural choices.

Administers, configures, and supports Jira, Confluence, GitHub, and other R&D tools. Builds projects, workflows, permissions, dashboards, reports, integrations, and documentation; evaluates and implements new Atlassian tools; supports R&D lab environments; partners with IT and Product teams; tests tool functionality; and develops user training and best practices.

Build and maintain full-stack enterprise SaaS features using .NET and Svelte or React. Design REST APIs and data models for Cosmos DB, SQL Server, and PostgreSQL. Collaborate with customers and internal teams to solve Microsoft Teams, telephony, and compliance challenges. Prototype, ship, iterate from feedback, and evaluate emerging technologies such as voice and text bots.

Own the roadmap and product strategy for DigiCert’s DNS and digital trust portfolio. Translate customer needs, market trends, telemetry, and technical capabilities into requirements, metrics, and secure cloud-native products. Partner with engineering, security, architecture, sales, marketing, and customers to deliver API-first infrastructure, validate concepts, launch products, and drive adoption. The role emphasizes trusted discovery, policy enforcement, reliability, security, experimentation, and emerging agentic use cases.

Design, develop, and maintain secure, scalable backend systems, APIs, databases, integrations, and services. Provide technical direction, lead architecture discussions, coordinate complex initiatives, manage dependencies, review code, improve performance and observability, respond to production incidents, maintain tests and CI/CD workflows, and mentor engineers. Collaborate remotely with Product, Operations, and engineering teams to deliver reliable software.

Design, build, and maintain scalable full-stack web applications and APIs using React, TypeScript, Next.js and Node.js. Own features end-to-end, collaborate with product and design, improve CI/CD and developer tooling, migrate legacy systems, and mentor junior engineers while leveraging AI-assisted development.

Design, build, test, and support enterprise software, APIs, automation workflows, and developer-facing platform capabilities. Develop secure, reliable, performant cloud-native solutions primarily on Azure and GCP. Contribute to architecture, engineering standards, testing, CI, and code reviews while collaborating across teams. Mentor colleagues through technical guidance and knowledge sharing, and help create self-service administration and developer productivity tools for the Maestro platform.

Develop full-stack enterprise application features using TypeScript, React, C#, and .NET, with emphasis on user experience, performance, scalability, and maintainability. Design and present technical solutions, build automated tests, prototype solutions, collaborate with engineering, product, and UX teams, and adopt emerging technologies including generative AI. The role also involves leading complex feature development, creating reliable APIs, and mentoring junior engineers.

Leads the architecture and hands-on evolution of Kinaxis Maestro’s cloud-native core platform. Designs distributed database, storage, query execution, and data-processing systems for massive scale, elasticity, performance, resilience, and cost efficiency. Guides architectural standards across Product R&D, prototypes and implements solutions, mentors senior engineers, and influences technical direction across teams. Requires deep expertise in distributed production systems, cloud platforms, concurrency, consistency, and high-performance backend development using Go, C++, or similar languages.

Owns the vision, strategy, roadmap, and outcomes for major Maestro platform capabilities. Leads customer discovery, platform architecture decisions, AI agent capabilities, data product integration, developer tooling, and enterprise adoption. Partners with engineering, data, cloud, and go-to-market teams to deliver scalable APIs, SDKs, services, and administration experiences. Requires 8+ years of product management experience, strong AI and modern data platform knowledge, technical fluency, and the ability to influence senior stakeholders.

Designs and leads scalable data architectures across cloud platforms, including data lakes, warehouses, analytics platforms, ETL/ELT pipelines, streaming ingestion, and data governance. Provides technical leadership, mentors engineers, optimizes performance and reliability, supports data migrations to Databricks Lakehouse, and advises clients on modernization and architecture decisions. Collaborates with stakeholders and analytics teams to deliver solutions for reporting, analytics, and AI initiatives.

Build and operate scalable data pipelines supporting batch and streaming workloads, AI and ML systems, analytics, and sports intelligence products. Responsibilities include ETL/ELT development, data modeling, orchestration, retrieval pipelines, data quality, lineage, monitoring, infrastructure optimization, and collaboration with ML, product, and analytics teams.

Designs and operates scalable ML infrastructure, including training, serving, inference, orchestration, and deployment pipelines. Optimizes GPU and CPU workloads, manages cloud clusters, implements observability and SLOs, and secures automated CI/CD and infrastructure-as-code workflows. Partners with research and AI engineering teams to productionize models, while building reusable tooling, templates, and runbooks. The role also supports low-latency model serving, data quality, model promotion, and cost optimization.

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.

Leads customer-facing digital forensics and incident response engagements, directing investigations, prioritizing containment and remediation, delegating analyst work, and coordinating with legal and insurance stakeholders. Conducts root cause and data-exfiltration analysis, manages multiple incidents, identifies MITRE ATT&CK techniques, mentors analysts, and produces executive reports with remediation guidance. The role focuses on complex incidents including ransomware, network breaches, business email compromise, insider threats, and cloud environments.

Serve as the primary DBA contact for critical production issues across customized, multi-tenant SQL Server environments. Diagnose performance and availability problems, optimize queries and stored procedures, monitor database health, build alerting and runbooks, maintain test-environment readiness, review database-related code changes, perform backups and patching, and document operational procedures. Collaborate closely with developers while providing production support during US business hours.

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 and maintains Retrieval-Augmented Generation systems, including document ingestion, indexing, semantic search, vector databases, embedding pipelines, and LLM integrations. Develops APIs and scalable knowledge systems, optimizes retrieval accuracy and performance, monitors production environments, and implements governance and security controls. The role also evaluates emerging retrieval technologies, troubleshoots indexing issues, documents procedures, and collaborates with AI engineers and product teams.

Leads the design, development, and optimization of enterprise Retrieval-Augmented Generation platforms. Responsibilities include building retrieval architectures, ingestion and indexing frameworks, semantic and vector search systems, evaluation methods, scalable APIs, and LLM integrations. The role also covers security, governance, compliance, monitoring, troubleshooting, documentation, technical leadership, mentoring, and evaluating emerging AI retrieval technologies.

Leads the architecture, development, deployment, governance, and optimization of enterprise Generative AI solutions. Builds applications using LLMs and foundation models, develops prompt engineering and evaluation strategies, and ensures secure, scalable production deployments. Partners with executives and business leaders to define AI roadmaps, mentors engineering teams, evaluates emerging technologies, and drives proof-of-concept initiatives and responsible AI practices.

Manage and optimize infrastructure supporting AI and machine learning systems, including cloud environments, GPU/CPU clusters, model deployment, data pipelines, security, monitoring, troubleshooting, automation, and CI/CD. The role develops scripts, maintains documentation, supports AI teams, and improves system reliability and scalability. Candidates need a relevant bachelor's degree, at least two years of systems administration or infrastructure experience, cloud and containerization expertise, scripting skills, and familiarity with machine learning frameworks and deployment pipelines.

Oversee and optimize infrastructure supporting AI and machine learning platforms, including servers, cloud environments, compute resources, GPU/CPU allocation, security controls, monitoring, and incident response. Design scalable AI environments, automate operational processes, troubleshoot system issues, and support model deployment and training workloads. Mentor junior administrators, evaluate emerging technologies, and recommend improvements to AI platform architecture, reliability, security, and scalability.

Develop and maintain cloud-based full-stack web applications using Angular, TypeScript, ASP.NET Core, Entity Framework Core, and Azure. Responsibilities include building responsive interfaces, developing RESTful APIs, writing unit tests, monitoring Application Insights, resolving defects, and delivering new platform functionality.

Deploy onsite with strategic customers to understand security and risk workflows, then build AI-powered integrations, data pipelines, and production software. Translate security ratings into financial risk models, connect systems across cloud environments, rapidly prototype and harden solutions, and guide technical relationships through renewal and expansion. The role bridges customers, Product, Engineering, Customer Success, and Sales Engineering while feeding field insights into the product roadmap.