Top AWS Jobs in Calgary

Build, maintain, and evolve Mozilla’s Internal Developer Infrastructure Platform: CI/CD, observability, Kubernetes optimization, environment bootstrapping, and cost optimization. Lead technical decisions, create Claude skills and agentic workflows to boost developer productivity, mentor teammates, write proposals, and deliver cross-functional platform features while ensuring usability, maintainability, and safe production paths.

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.

Build and own backend services, ERP and supplier integrations, AI agentic workflows, spend analytics, and customer-facing procurement features. Develop evaluation and monitoring systems for production AI, establish architecture, improve security and privacy, and mentor junior engineers. Collaborate directly with customers and founders to shape product direction in an early-stage environment.

Design and deliver backend software automating post-trade processing, including clearing, settlement, reconciliation, and depository integrations. Contribute to technical architecture, mentor developers through pairing and code reviews, and collaborate with Securities Operations, Product Management, and engineering teams. Build accurate, auditable systems for high-stakes financial workflows while expanding pipelines for new products.

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.

Build and optimize production-grade agentic AI systems for marketing and enterprise customer experience use cases. Responsibilities include agent orchestration, tool use, RAG, embeddings, guardrails, LLM evaluation, observability, performance optimization, and scalable Python services deployed on AWS. The role partners across product and engineering teams and requires hands-on experience with LLM agents, marketing data platforms, retrieval systems, and AI evaluation.

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.

Design, build, and operate services powering card issuance, transaction authorization, wallet provisioning, 3-D Secure, and disputes. Own features end-to-end within a microservice, event-driven architecture, write well-tested code, participate in reviews, and perform operational ownership including on-call and incident response. Leverage AI developer tools to improve velocity and quality.

Design, build, and operate CIAM backend services for registration, authentication, authorization, and account lifecycle. Implement OAuth/OIDC/SAML/SCIM, develop APIs in Python/Kotlin, integrate identity platforms, automate deployments with IaC and CI/CD, and ensure secure, reliable, high-scale identity operations.

Build the enterprise platform for an AI creative product, including workspaces, permissions, SSO, audit logs, usage-based billing, invoicing, payment integrations, and security foundations. Develop backend services, APIs, and polished React/Next.js administrative interfaces for multi-tenant customers. Collaborate with founders, sales, and customers to define and ship enterprise features while taking increasing ownership of platform surfaces.

Build and operate high-throughput backend services powering AI agent orchestration, connector integrations, and real-time data pipelines. Design core platform services, infrastructure-as-code, CI/CD, and scalable APIs. Collaborate with ML, frontend, and product teams to ship AI-powered features, mentor junior engineers, and maintain high availability for consumer and enterprise customers.

Lead and grow a Maps & Search engineering team responsible for the home search experience across web and mobile. Stay hands-on with coding and architecture, scale a high-throughput search platform (~1B monthly requests), improve relevance and latency, integrate AI/LLMs into discovery and ranking, shape product roadmap, and own delivery, SLOs, and incident response.

Design, build, test, ship, and operate services powering card issuance, transaction processing, and disputes. Own features end-to-end within a microservice and event-driven architecture, write well-tested code, participate in on-call incident response, and leverage AI tools to improve development velocity and quality while collaborating with cross-functional teams.

Lead product vision and strategy for complex, high-stakes digital products in client engagements. Serve as the senior product advisor to executives, define operating models and delivery standards, guide discovery and roadmapping, resolve complex product decisions, and establish success metrics. Mentor experienced product managers, influence engineering and design teams, support business development, and improve product management practices. Apply AI tools such as Claude to increase efficiency and strengthen research and product thinking.

Own the technical sales motion for NVIDIA-focused enterprise AI infrastructure deals. Lead discovery, architecture, proof-of-concept design and execution, technical closes, partner and field SE enablement, troubleshooting, escalation, customer workshops, and product feedback. The role requires expertise in NVIDIA systems, storage, data platforms, Linux, parallel file environments, GPU clusters, and AI pipelines, with Bay Area availability for on-site partner or customer sessions.

Build AI-powered features that automate data-driven slide decks and documents. Develop production LLM solutions using LangChain, RAG, LlamaIndex, and generative AI technologies; improve system performance and reliability; and collaborate with designers and engineers. Responsibilities span AI insights, content generation, integrations, optimization, and potentially LLM agents. The role requires advanced Python, machine learning and NLP expertise, and experience building and fine-tuning LLMs in production.

Manage end-to-end AWS and Anthropic funding program workflows, including documentation, funding requests, invoice preparation, compliance checks, approvals, and deadline tracking. Maintain accurate client records and operational systems, communicate program updates, coordinate with internal teams and external AWS stakeholders, resolve documentation issues, and improve efficiency through reporting tools and automation.

Designs and improves large-scale data processing systems and GCP workflows using Dataflow, BigQuery, Pub/Sub, GKE, and Airflow. Evaluates technologies, establishes engineering standards, leads cross-functional data projects, develops data abstractions with Data Science, mentors engineers, and supports evolving data products.

The Quality Assurance Analyst performs manual and automated testing throughout the software development lifecycle. Responsibilities include reviewing requirements, creating test plans and cases, executing functional, integration, regression, end-to-end, and exploratory tests, managing defects, maintaining automation suites, supporting CI/CD integration, and reporting quality metrics. The role also applies AI-assisted testing tools to improve test coverage, identify risks, analyze defects, and enhance QA processes while collaborating with product, development, and support teams.

Consultative Sales Engineer responsible for driving complex sales opportunities, designing cloud data security and data risk management solutions, delivering proof-of-value engagements and technical demonstrations, advising executive stakeholders, supporting partner channels, and coordinating cross-functional resources. The role requires cloud security, IAM, networking, databases, encryption, privacy, data governance, and strong technical communication skills.