naukri
AI Operation Engineer
B-informative It Services
Saudi Arabia, KSA
Contract
Senior
Onsite
6 days ago
Linux AdministrationGPU InfrastructureLLM Inference EnginesInfrastructure as CodeAnsiblePython
Free
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Linux AdministrationGPU InfrastructureLLM Inference Engines
About the Role
Operate and maintain GPU-accelerated AI inference platform, focusing on infrastructure provisioning, model deployment, and performance optimization with strong Linux and NVIDIA.
Key Skills for This Role
Linux AdministrationGPU InfrastructureLLM Inference EnginesInfrastructure as CodeAnsiblePython
Responsibilities
- Provision, harden, and patch vanilla Linux, installing GPU drivers, Python, and inference engines
- Maintain version controlled infrastructure as code for repeatable fleet deployments
- Deploy and maintain LLM inference serving engines across enterprise GPUs
- Configure model sharding, quantization, and GPU memory/cache sizing
- Set up and troubleshoot tool calling, reasoning modes, and multimodal serving
- Configure routing, load balancing, API keys, token counting, and usage tracking
- Own metrics collection, dashboards, and GPU level telemetry
- Report on throughput, latencies, queue depth, and tokens per second
- Deliver token level cost analysis and usage reporting per user and application
- Analyze throughput across GPU configs to guide procurement and fleet planning
- Validate new models and quantization methods before production rollout
- Resolve performance bottlenecks across drivers, runtimes, and network layers
Requirements
- Minimum 4 years of hands on experience in Linux systems engineering
- At least 2 years involving GPU infrastructure or ML/AI workloads
- Demonstrated experience deploying and operating LLM inference engines in production
- Strong working knowledge of NVIDIA GPU software stack
- Experience with infrastructure as code and configuration management tools
- Solid understanding of Linux containerization technologies
- Ability to work independently and take ownership
- Clear technical communication skills
Full Job Posting
Position Overview
- We are seeking an AI Operations Engineer to operate and maintain a GPU accelerated AI inference platform.
- This is a hands on technical role responsible for the day to day operations of LLM serving infrastructure.
- The platform serves large language models to internal applications.
Key Responsibilities
- Infrastructure Provisioning & Automation: Provision, harden, and patch vanilla Linux, installing GPU drivers, Python, and inference engines.
- IaC Automation: Maintain version controlled infrastructure as code for repeatable fleet deployments.
- Air Gapped Workflows: Manage offline, restricted network deployments and local model distribution.
- Container Management: Configure containerized workloads, database backends, and reverse proxies.
- LLM Inference Engine Operations: Deploy and maintain LLM inference serving engines across enterprise GPUs.
- Multi GPU Sharding: Configure model sharding based on size, topology, and workload requirements.
- Quantization & Sizing: Manage quantization trade offs and calculate GPU memory/cache sizing for optimal concurrency.
- Feature Config: Set up and troubleshoot tool calling, reasoning modes, and multimodal serving.
- Model Evaluation: Benchmark and deploy new model releases for quality, accuracy, and throughput.
- API Gateway & Traffic Management: Configure routing, load balancing, API keys, token counting, and usage tracking.
- Monitoring, Reporting, & Cost Analysis: Own metrics collection, dashboards, and GPU level telemetry.
- Capacity Planning & Performance Engineering: Analyze throughput across GPU configs to guide procurement and fleet planning.
Required Qualifications
- Minimum 4 years of hands on experience in Linux systems engineering, with at least 2 years involving GPU infrastructure or ML/AI workloads.
- Demonstrated experience deploying and operating LLM inference engines in production environments.
- Strong working knowledge of the NVIDIA GPU software stack: drivers, toolkits, runtime libraries, and common failure modes.
- Experience with infrastructure as code and configuration management tools.
- Solid understanding of Linux containerization technologies, including rootless operation and service management integration.
Technical Skills
- Operating Systems: Linux administration (RHEL family preferred), including kernel tuning, service management, storage, and network configuration.
- GPU and AI Stack: NVIDIA drivers and toolkits, LLM inference engines, model quantization techniques, multi GPU parallelism concepts.
- Networking: HTTP/HTTPS, reverse proxy configuration, TLS/SSL, firewall management, network file systems, and load balancing concepts.
- Monitoring: Metrics collection, visualization, and alerting using industry standard observability tools.
- Automation: Configuration management (Ansible preferred), shell scripting, Python for operational tooling.
- Version Control: Git with disciplined branching, tagging, and commit practices.
- Python: Virtual environments, dependency management, and debugging Python based services.
Compensation & Benefits
- Fully Covered Accommodations: Premium housing/accommodation provided.
- Meals & Dining: Full food and dining arrangements taken care of by the company.
- Travel & Commute: Comprehensive coverage of international flight tickets and all local business related transportation.
- Visa Sponsorship: End to end processing and cost management for work visas and legal documentation.
- Per Diem Allowance: A daily out of pocket allowance (Per Diem) will be provided.
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