AI Engineer
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What you'll do
- Develop, deploy, and operate AI/LLM models across Clinets dual environment — GCP for public-cloud workloads, Humain sovereign cloud for classified data.
- Build and fine-tune LLM/ML models for Arabic NLP, document classification, vision/OCR, and AIOps use cases.
- Run pre-deployment evaluation
- Optimize inference — quantization, batching, context sizing — against measured usage.
- Deploy on Humain GPUaaS: Kubernetes, GPU partitioning on B300 nodes, quotas, RBAC.
- Build equivalent workloads on GCP (Vertex AI, GKE) with classification-based routing.
- Own serving stack (vLLM/TGI), model versioning, CI/CD, and monitoring for latency, tokens, GPU utilization, and drift.
- Ensuring developed AI Models Complying with ZATCA data sovereignty and SDAIA requirements (AI Ethics, GenAI Guidelines, PDPL).
What they're looking for
- Build and fine-tune LLM/ML models for Arabic NLP, document classification, vision/OCR, and AIOps use cases.
- Run pre-deployment evaluation
- Optimize inference — quantization, batching, context sizing — against measured usage.
- Deploy on Humain GPUaaS: Kubernetes, GPU partitioning on B300 nodes, quotas, RBAC.
- Build equivalent workloads on GCP (Vertex AI, GKE) with classification-based routing.
- Own serving stack (vLLM/TGI), model versioning, CI/CD, and monitoring for latency, tokens, GPU utilization, and drift.
- Ensuring developed AI Models Complying with ZATCA data sovereignty and SDAIA requirements (AI Ethics, GenAI Guidelines, PDPL).
- 5 years ML/AI engineering, in production LLM deployment with knowledge in
- Python, PyTorch, Hugging Face
- Kubernetes in production; GPU-served inference
- GCP Vertex AI or any equivellent cloud
Summarised by NextRaise from the employer’s description, which follows in full below.
Full description from employer
Looking for an AI Engineer to Develop, deploy, and operate AI/LLM models across Clinets dual environment — GCP for public-cloud workloads, Humain sovereign cloud for classified data.
Requirements
Build and fine-tune LLM/ML models for Arabic NLP, document classification, vision/OCR, and AIOps use cases.
Run pre-deployment evaluation
Accuracy baselines, regression and safety testing; evidence to justify GPU allocation.
Optimize inference — quantization, batching, context sizing — against measured usage.
Deploy on Humain GPUaaS: Kubernetes, GPU partitioning on B300 nodes, quotas, RBAC.
Build equivalent workloads on GCP (Vertex AI, GKE) with classification-based routing.
Own serving stack (vLLM/TGI), model versioning, CI/CD, and monitoring for latency, tokens, GPU utilization, and drift.
Ensuring developed AI Models Complying with ZATCA data sovereignty and SDAIA requirements (AI Ethics, GenAI Guidelines, PDPL).
Benefits
5 years ML/AI engineering, in production LLM deployment with knowledge in
Python, PyTorch, Hugging Face
Kubernetes in production; GPU-served inference
GCP Vertex AI or any equivellent cloud
Company
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