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Infrastructure & DevOps Engineer

Precision Ai

CalgaryHybrid1mo ago
Employment
Full-time

About the role

  • Design, build, and maintain automated CI/CD pipelines using GitHub Actions; implement deployment automation across development, staging, and production environments. 
  • Support and optimize AWS infrastructure; manage cloud resources, monitoring, and cost optimization. 
  • Clean up and refactor systems architecture to improve reliability, scalability, and maintainability. 
  • Triage and resolve infrastructure issues across AI infrastructure, website platforms, and agentic AI tools; prioritize production incidents and system reliability improvements. 
  • Monitor system health, respond to incidents, and implement fixes in a high-velocity startup environment. 
  • Maintain runbooks and infrastructure documentation to enable team self-sufficiency. 
  • Implement basic QA processes and automated testing within CI/CD pipelines. 
  • Collaborate with AI/ML teams on infrastructure requirements, deployment needs, and tooling support. 
  • 3-5+ years of professional experience in DevOps, EngOps, or infrastructure engineering roles at startups with demonstrated automation and systems reliability experience. 
  • Strong proficiency with GitHub Actions for CI/CD pipeline development and automation. 
  • Proficiency in scripting (Python/Bash) and infrastructure-as-code practices; experience debugging web applications and cloud systems. 
  • Hands-on experience managing AWS infrastructure (EC2, S3, Lambda, RDS, CloudWatch, etc.) in production environments. 
  • Experience diagnosing connectivity issues using tools like ping, curl, traceroute, and log analysis  
  • Comfortable handling large image datasets (e.g., on S3), CSV/JSON files, and performing data quality checks and validation. 
  • Ability to identify and resolve CPU/memory bottlenecks, slow services, and system performance issues. 
  • Basic Java and Linux experience 
  • Bias for action and excellence in maintenance work within scrappy, resource-constrained environments. 
  • Ability to anticipate downstream issues and handle second-order effects independently without constant oversight. 
  • Collaborative yet autonomous working style; excited by variety and breadth over narrow specialization. 
  • Attention to detail in troubleshooting, performance monitoring, and incident documentation. 
  • Startup background with end-to-end ownership of support across multiple teams. 
  • Exposure AI/ML infrastructure, model deployment pipelines, or agentic AI tools. 
  • Exposure to edge AI deployment on NVIDIA Jetson platforms, including running and testing GPU-accelerated workloads (CUDA/TensorRT). 
  • Experience deploying, running, and troubleshooting containerized (Docker) applications. 


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