Key Responsibilities
Systems Integration & Reliability
- Deploy, configure, and validate edge computing systems across lab, field, and production environments.
- Integrate and optimize system components spanning embedded hardware, networking, containerization, and cloud APIs.
- Collaborate with software, infrastructure, and field teams to identify and resolve integration and runtime issues.
- Ensure reliable device-to-cloud communication for telemetry, control, and analytics workloads.
Troubleshooting & Diagnostics
- Perform end-to-end triage across hardware, network, and application layers.
- Use Linux CLI tools, container inspection, and telemetry analysis to isolate and correct complex system failures.
- Reproduce field issues in controlled environments and contribute findings back into engineering processes.
- Develop reusable diagnostic tools and test harnesses to validate system resilience.
Automation & Observability
- Build and maintain monitoring, and recovery automation (e.g., Bash, Python, Go).
- Contribute to orchestration frameworks such as Docker, K3s, or Kubernetes for edge deployments.
- Enhance observability through metrics, dashboards, and alerting (Datadog, Grafana, Prometheus, etc.).
- Identify opportunities for self-healing and reliability automation.
Knowledge Management
- Author and maintain runbooks, standard operating procedures, and knowledge base articles.
- Document troubleshooting procedures and design patterns to enable Tier 1 and Tier 2 support efficiency.
- Participate in post-incident reviews and translate lessons learned into durable operational improvements.
Collaboration & Escalation
- Partner with software engineers, DevOps, and operations teams to drive incident resolution.
- Act as a 24x7 escalation SME for complex edge or connectivity issues.
- Leverage escalation learnings to define and drive system reliability and lifecycle management initiatives.
Safety
- Adhere to all NOV HSE policies, utilize appropriate PPE, and actively participate in monthly safety meetings.
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QualificationsÂ
Required
- Bachelor’s degree in Computer Engineering, Computer Science, Information Systems, or equivalent work experience
- 3-5 years minimum relevant experienceÂ
- Strong proficiency with Linux systems and command-line diagnostics.
- Experience with containerized environments (Docker, K3s, or Kubernetes).
- Understanding of IoT or distributed systems architectures, including secure communication (TLS/mTLS).
- Solid grasp of networking fundamentals: IP, routing, VPNs, DNS, and cellular/LTE connectivity.
- Scripting ability in Bash, Python, or Go for automation and tooling.
- Demonstrated ability to troubleshoot across hardware, network, and software boundaries.
- Excellent written communication skills; comfortable producing procedural documentation.
Preferred
- Experience in industrial or edge computing environments (IoT gateways, embedded Linux, or ruggedized hardware).
- Familiarity with GitOps workflows, CI/CD pipelines, or infrastructure-as-code tools.
- Exposure to observability platforms such as Datadog, Grafana, or Prometheus.
- Background in site reliability, DevOps, or systems integration roles supporting production systems.
 Ideal Profile
You are a multidisciplinary engineer who:
- Moves fluidly between hardware, software, and networking domains.
- Believes operational excellence and reliability are core features of a product.
- Automates repetitive work and documents what cannot be automated.
- Brings composure, curiosity, and methodical problem-solving to complex technical challenges.
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Why This Role Matters
Edge computing is rapidly redefining how distributed systems are deployed and managed.
The Edge Systems Engineer ensures those systems are reliable, observable, and maintainable — providing the connective tissue between development, infrastructure, and real-world operations.
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