Jobgether
Senior Support Engineer
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Senior Support Engineer
Senior Support Engineer – Cloud & AI Infrastructure (UK-based) – "Critical Role in AI/ML & Distributed Systems Support"
About the Role
This position is available through a partner company based in United Kingdom. As a Senior Support Engineer, you’ll operate at the heart of production-grade cloud environments, focusing on AI, distributed computing, and GPU workloads.
This hands-on role demands deep technical expertise in diagnosing, escalating, and resolving complex infrastructure issues across:
- Linux systems
- Kubernetes and containerised environments
- Networking, storage, and GPU-based architectures
You’ll act as the lead escalation point for critical incidents, collaborating directly with engineering and customers to restore system stability, conduct root cause analysis, and drive permanent improvements. Beyond traditional support, you’ll contribute to: ✔ Enhancing observability and monitoring tools ✔ Automating troubleshooting workflows ✔ Optimising operational maturity across large-scale cloud platforms
This role suits individuals who thrive in dynamic environments, enjoying the challenge of unambiguous, high-stakes technical problem-solving.
Key Accountabilities
Diagnosis & Resolution
- Investigate, troubleshoot, and resolve high-impact production issues with root cause analysis as a top priority.
- Debug multi-layered systems, including:
- Linux environments (performance, logging, misconfigurations)
- Kubernetes clusters (node performance, pod behaviour, scaling)
- Networking layers (latency, packet loss, distributed traffic issues)
- Storage systems (I/O bottlenecks, cluster replication)
- GPU-accelerated workloads (driver issues, resource contention)
Escalation & Collaboration
- Serve as the senior escalation point for critical incidents, ensuring rapid resolutions.
- Work closely with engineering teams to:
- Reproduce and mitigate issues
- Identify systemic dependencies and drive long-term fixes
- Support customer-facing incidents, including AI/ML pipelines and inference/training workloads.
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Tooling & Automation
- Develop and improve internal tools (automation scripts, dashboards) in:
- Python, Bash, Go, or equivalent languages.
- Enhance scripting efficiency for repetitive troubleshooting tasks.
Observability & Reliability
- Contribute to operational excellence by:
- Advocating for better monitoring and alerting mechanisms.
- Streamlining debugging workflows through structured post-incident reviews.
- Improve platform reliability and scalability.
Incident Response
- Participate in 24/7 incident-response rotations, including weekend on-call shifts.
Requirements
Technical Expertise
- Strategic Linux administration (RHEL, Ubuntu, debugging kernel/logs).
- Kubernetes expertise, including:
- Deep knowledge of resource scheduling, networking (CNI), and cluster management.
- Experience with self-hosted vs cloud-managed clusters.
- Cloud infrastructure proficiency:
- AWS, GCP, Azure, or OpenStack ( ประจำการ ที่ในที่ตั้งว่าง).
- Hands-on experience with orchestration, scaling, and cross-service dependencies.
- Networking fundamentals, with skills in:
- Troubleshooting complex distributed networks (routing, DNS, load balancing).
- Latency/connectivity patterns in containerised and cloud-native environments.
Troubleshooting & Debugging
- Ability to successfully reproduce and correct incidents under pressure.
- Capability to ascertain root causes from logs, metrics, and distributed tracing.
- Strong collaboration in cross-team scenarios (DevOps, SRE, platform teams).
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Automation & Scripting
- Scripting in Python/Bash/Go for:
- Automating repetitive tasks (diagnostics, infrastructure validation).
- Building lightweight monitoring or alerting scripts.
AI & GPU Experience (Highly Desirable)
- Prior experience with GPU-based computing (CUDA, drivers, resource contention).
- Work on AI/ML workloads, including:
- Model training pipelines and inference.
- Debugging end-to-end distributed AI models.
Soft Skills
- Analytical thinking for ambiguous, high-pressure scenarios.
- Clear, non-technical-friendly communication of technical issues.
- Ability to documents findings for internal learning/improvement.
Benefits & Perks
- Competitive compensation (aligned with experience and expertise).
- Career growth opportunities, with emphasis on learning and technical development.
- Flexible working, high autonomy, and ownership over systems.
- Exposure to cutting-edge technology, including:
- Large-scale distributed systems.
- AI-driven infrastructure.
- Collaborative environment with skilled, internationally diverse engineering teams.
- Impact-oriented role, operating at a cross-section of modern cloud and AI infrastructures.
- Inclusive and innovation-driven culture, focused on:
- Continuous improvement.
- Best practices, not process bureaucracy.
How Jobgether Works
This position is managed through an AI-matching process for fair and efficient candidate reviews. Applied candidates are screened against technical fit, then shortlisted directly for the partner company’s internal hiring team, who handle interview scheduling, assessments, and next steps.
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