Top AWS Jobs in Calgary

Build and maintain scalable integration and end-to-end test automation frameworks and CI pipelines. Optimize test execution, improve stability and reporting, support engineering teams with triage and best practices, write technical specs/ADRs, participate in on-call rotation, and collaborate across regions to evolve the companys test strategy and developer productivity tooling.

Lead the architecture, scaling, reliability, and security of multi-region AWS infrastructure supporting high-throughput payment systems. Build immutable, self-healing environments with Terraform, Kubernetes, GitOps, CI/CD, and automated resiliency. Develop observability using Prometheus, Grafana, and OpenTelemetry; manage incidents and post-mortems; improve engineering velocity through Golden Paths and platform enablement; and mentor engineering teams on reliable infrastructure practices.

Lead development of next-generation AI systems for insurance automation, including document validation, voicebots, computer-use agents, and complex workflow orchestration. Set chatbot architecture and technical standards, build and deploy production automations, evaluate emerging AI technologies, and partner with OpenAI on voice models. Own LLM system reliability, including evaluations, regression prevention, latency, cost, and failure handling.

Build and lead next-generation AI systems for automated insurance quoting, customer support, document validation, voice interactions, and external system navigation. Set technical direction for chatbot architecture, evaluate emerging AI technologies, and deploy production LLM systems. Own automation outcomes end to end, including evaluation, regression prevention, latency, cost, and failure handling, while partnering with OpenAI on voice models and workflow automation.

Lead the deployment of AI solutions for public sector clients, ensuring compliance with security standards while integrating AI into enterprise workflows.

Develop and optimize machine learning and statistical models for fraud detection and anti-money laundering. Analyze large datasets to identify fraudulent patterns, build scalable data pipelines and features, evaluate model performance, and collaborate with engineering, product, and operations teams. Communicate insights to technical and non-technical stakeholders while maintaining data privacy and security compliance. The role offers significant ownership in building Oscilar’s early machine learning stack within a remote-first startup.

Designs, builds, and deploys production AI solutions for legal and compliance workflows. Partners with legal stakeholders to develop LLM, RAG, agentic AI, document-processing, search, evaluation, governance, security, and compliance applications. Writes tested Python or TypeScript code, manages AI infrastructure and deployment, establishes quality frameworks, and mentors legal users on no-code AI solutions while collaborating with IT and data teams.

Lead front-office investment technology implementations from current-state analysis and requirements through workflow design, configuration, integration, testing, training, and handover. Manage project plans, statements of work, governance, budgets, client communications, and teams of developers and consultants. Advise executive stakeholders on technology choices, operating models, data architecture, migration, quality assurance, and best practices across asset and wealth management platforms.

Lead enterprise-wide programs transforming contact center and telephony platforms from Avaya to AWS Connect. Manage scope, schedules, budgets, resources, risks, dependencies, and cross-functional teams. Coordinate project managers and business analysts, provide executive dashboards and steering committee updates, resolve escalated issues, and support training and process improvement. The role requires strong program leadership, executive communication, and experience delivering complex IT initiatives in high-visibility environments.

Lead a team of software engineers and architects delivering complex, AI-first cloud applications for enterprise clients. Responsibilities include people management, requirements gathering, architecture, backlog ownership, technical decision-making, AI-driven testing, client discovery, risk escalation, executive communication, mentoring, and pre-sales support. The role requires hands-on AWS cloud-native development, consulting expertise, and leadership across large, multi-stakeholder engagements.

Build and operate high-volume telemetry and analytics data pipelines supporting real-time monitoring, product decisions, experimentation, and user experience analysis. Design event instrumentation, schemas, quality checks, deduplication, late-event handling, and identity stitching. Partner with data science, product, and SRE teams to shape requirements, and integrate AI tooling into development and data quality workflows.

Design and develop advanced machine learning and generative AI solutions, including LLM fine-tuning, RAG architectures, prompt engineering, model evaluation, and MLOps. Architect scalable training, deployment, and retraining workflows on AWS using SageMaker and related services. Collaborate with engineering, QA, project management, and other stakeholders to deliver reliable AI applications and solve complex business problems.

Own the Android app end to end, including architecture, performance, releases, app store presence, experimentation, and roadmap. Lead and mentor mobile engineers, establish technical direction, and deliver polished native experiences. Partner with engineering and product leadership on growth, onboarding, and retention initiatives. Ship, instrument, measure, and iterate quickly in a PLG and A/B testing environment, while helping scale the mobile team.

Lead design, implement, and maintain a scalable SaaS insurance platform across front-end and back-end. Provide technical direction, mentor developers, enforce best practices, optimize performance, ensure security and compliance, and drive adoption of modern architectures and testing methodologies.

Lead end-to-end predictive analytics and machine learning initiatives, including forecasting, churn prediction, and risk modeling. Translate business needs into scalable ML solutions, build and deploy models on AWS SageMaker, develop production ML pipelines, and oversee monitoring, governance, explainability, and retraining. Collaborate with engineering and MLOps teams, communicate insights to stakeholders, and mentor junior data scientists while providing hands-on technical leadership.

Lead architecture and hands-on development for risk, fraud, scam prevention, money operations, reconciliations, and chargebacks. Build resilient, low-latency, event-driven financial systems with auditable ledgers, strong correctness, and high availability. Mentor engineers, establish technical direction, support production ownership, and collaborate with Product, Data/ML, Compliance, and engineering leaders. Apply AI-assisted tooling and deliver scalable, compliant services across global payment infrastructure.

As a Staff Data Scientist, you will analyze large-scale broadband data, develop and validate ML models, and integrate solutions into Calix Cloud, collaborating with teams for enhanced network intelligence and subscriber experience.

Lead application migration and modernization initiatives to AWS, utilizing cloud-native practices and providing technical leadership for development teams.

Lead and grow the Core Workspaces engineering team, set technical direction, and contribute to architecture and implementation. Build reliable, extensible agentic systems involving agent execution, tool use, context management, streaming, integrations, and long-running workflows. Partner with Product and Design to improve developer experiences while coaching engineers, raising technical standards, and optimizing reliability, performance, and operability.

Design and operate scalable, cloud-native data pipelines and orchestration systems for imaging, biomarker, and healthcare data. Build Databricks and distributed processing workflows, integrate DICOM data into data lakes, and implement validation, observability, monitoring, and fault tolerance. Collaborate with data science, AI, R&D, product, and data management teams to deliver secure, compliant, reliable data for analytics and machine learning.

Owns the cloud architecture vision and roadmap for an AWS-first, multi-cloud SaaS environment. Designs secure, resilient, scalable, and cost-efficient infrastructure; leads cloud modernization; enables AI and data platforms; establishes Terraform, CI/CD, observability, security, governance, and reliability standards; and partners with engineering, security, operations, customers, auditors, and leadership. Coordinates major incident response and drives self-service infrastructure and developer experience improvements.

The Artificial Intelligence Engineer at Precision AI will lead AI/ML projects, develop and optimize machine learning models, and ensure technical quality while mentoring junior engineers.

Design, train, and deploy ML and NLP models (including custom transformers) using modern tooling; prepare and transform large datasets; monitor production pipelines; communicate results to stakeholders; ensure model reliability, explainability, and ethical alignment.

Own the technical side of deal progression for airline deployments: qualify opportunities, run demos and POCs, author integration and deployment architectures (PSS, APIs, data feeds, DNS, identity, networking), respond to RFPs/security questionnaires, scope SOWs and NFRs, and build reusable demo/reference assets to drive adoption and expansion while liaising between sales, engineering, and customers.

Build scalable infrastructure for machine learning training, deployment, monitoring, retraining, and continual learning. Responsibilities include distributed training pipelines, dataset and annotation workflows, experiment tracking, model versioning, benchmarking, CI/CD, cloud-to-edge deployment, inference APIs, and embedded NVIDIA platform optimization. The role collaborates with computer vision, navigation, embedded software, and cloud engineering teams to move AI research into reliable production systems.