AI Inference & Engine Optimization Intern (AI Training / PyTorch/CUDA)
About Siellium Global Labs
Siellium Global Labs Private Limited is a startup scaling a zero-retention infrastructure layer across sovereign enterprise networks. We are engineering a local-first execution layer that utilizes a dynamic Mixture-of-Agents (MoA) orchestration framework.
The Role:
We are looking for a highly capable AI Inference & Optimization Intern to build and manage our local inference pipelines. This is a pure backend infrastructure role. You will not be building standard UI wrappers; you will be directly manipulating GPU memory, optimizing token throughput, and deploying a high-performance Mixture-of-Agents (MoA) execution layer.
Your mission is to squeeze maximum computational power out of our hardware. You will be responsible for pinning base models into VRAM and executing the mathematical compression needed to allow multiple domain-specific expert models to swap in and out seamlessly on a per-request basis.
Stand up and operate the core execution engine using vLLM, explicitly utilizing custom PagedAttention layers to maximize GPU concurrency and throughput.
- Memory Optimization: Execute mathematical compression on neural networks to fit them into constrained hardware budgets. You will apply structural matrix pruning algorithms and execute Activation-Aware Weight Quantization (AWQ) to compress 16-bit floating-point weights down to 4-bit and 8 bit Integers.
- Dynamic Adapter Swapping/Orchestration: Pin a foundational base model (Llama-3-8B) securely into VRAM. Configure the asynchronous, agile hot-swapping of specialized Low-Rank Adapters (LoRA) for domain experts like DeepSeek-Coder (for syntax) and Phi-4 (for logic)
- Continuous Batching: Tune the inference server to handle high-volume asynchronous requests, preventing memory bottlenecks during multi-model orchestration loops.
- Pipeline Integration: Ensure se
📌 AI Trainer and Architecture Intern (India)
🏢 Siellium Global Labs Private
📍 India
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