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JAMS Platform Engineer

  • Toronto, ON, Canada

$65 to $70 CAD / hour · Contract

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Job details

Pay
$65 to $70 CAD / hour
Job type
Contract
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Posting language
English
Posted

Original job description

Pay Range: CAD 65-70/hr

Seeking a hands-on engineer to drive the reliability| automation and modernization of the enterprise JAMS workload automation platform. The role combines JAMS platform expertise with software engineering| automation and AI capabilities.Key ResponsibilitiesEngineer and administer JAMS Scheduler| including Scheduler| Agents| Executors| queues| resources and job configuration. Improve platform reliability| performance| capacity| resilience and observability. Lead complex troubleshooting| performance analysis and root-cause investigations. Build proactive monitoring and automated recovery for Scheduler| agents| queues and batch workflows. Develop automation using Python| PowerShell| APIs and scripting to reduce manual operational effort. Build and maintain engineering solutions using GitHub| including source control| code review and CI/CD practices. Drive migration from time-based to event-driven scheduling and simplify batch orchestration. Establish engineering standards for retries| long-running jobs| alerts| resources| agents and job design. Support upgrades| patching| regression testing| DR and production readiness. Partner with BatchOps| application and infrastructure teams to onboard and optimize workloads. Work with the JAMS vendor on product defects| performance and roadmap capabilities. Explore and implement practical AI use cases for operational analytics| anomaly detection| troubleshooting and automation.

Technical Skills

Strong hands-on JAMS Scheduler / workload automation experience Python and PowerShell GitHub / Git| CI/CD and software engineering practices REST APIs and systems integration SQL Server and database troubleshooting Windows Server and distributed application troubleshooting Monitoring| telemetry and performance engineering Exposure to AWS/cloud technologies preferred Practical understanding of AI/GenAI tooling and willingness to apply AI to operational engineering Success in the Role Move JAMS from scheduler administration/operational role toward an engineered| observable and increasingly automated platformreducing manual intervention while improving resilience and recovery.

More about this job

Responsibilities

Engineer and administer the JAMS workload automation platform, improving its reliability, performance, resilience, observability, and recovery through monitoring, troubleshooting, and automation. Modernize batch orchestration, support platform upgrades and production readiness, collaborate with application and infrastructure teams and the vendor, and apply practical AI capabilities to operational engineering.

Requirements

The role calls for hands-on JAMS Scheduler and workload automation experience, along with Python or PowerShell scripting, GitHub and CI/CD practices, REST APIs, SQL Server, and Windows Server troubleshooting. Candidates should also bring monitoring and performance engineering experience; exposure to cloud technologies and practical familiarity with AI or generative AI tools are preferred.

Skills

  • JAMS Scheduler
  • Workload Automation
  • Python
  • PowerShell
  • GitHub
  • Git
  • CI/CD
  • REST APIs
  • SQL Server
  • Windows Server
  • Monitoring and Observability
  • Performance Engineering
  • Cloud Technologies
  • AI and Generative AI
  • Batch Orchestration
  • Root-Cause Analysis

Visa sponsorship

Not detected in the job text

Categories

  • Technology
  • Software
  • Engineering
  • Data & Analytics

Keywords

  • JAMS Scheduler
  • Workload Automation
  • Scheduler Administration
  • Agents
  • Executors
  • Queues
  • Resources
  • Python
  • PowerShell
  • GitHub
  • Git
  • CI/CD
  • REST APIs
  • Systems Integration
  • SQL Server
  • Windows Server
  • Distributed Applications
  • Monitoring
  • Telemetry
  • Performance Engineering
  • Event-Driven Scheduling
  • Batch Orchestration
  • Automated Recovery
  • Regression Testing
  • Disaster Recovery
  • AWS
  • Cloud Technologies
  • AI
  • Generative AI
  • Anomaly Detection

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