LLM Inference Deployment Engineer

Job not on LinkedIn

🕒 May 21

🇺🇸 United States – Remote

💵 $180k - $240k / year

⏰ Full Time

🟡 Mid-level

🟠 Senior

⛑ DevOps & Site Reliability Engineer (SRE)

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EnCharge AI

WebsiteLinkedInAll Job Openings

11 - 50 employees

Founded 2022

🤖 Artificial Intelligence

🔧 Hardware

🤝 B2B

💰 $100M Series B - EnCharge AI on 2025-02

Artificial Intelligence • Hardware • B2B

EnCharge AI is a company that develops analog in-memory computing hardware and complementary software to accelerate on-device and edge-to-cloud AI workloads. Their technology includes the EN100 analog AI accelerator and other form factors (chiplets, ASICs, PCIe cards) designed to deliver much higher energy efficiency, compute density, and lower total cost of ownership for inference compared with conventional GPUs and digital accelerators. EnCharge emphasizes sustainability, data privacy through local processing, and deployment for enterprise and developer customers seeking efficient, scalable AI computation outside traditional cloud infrastructure.

📋 Description

• Deploy and optimize LLMs (GPT, LLaMA, Mistral, Falcon, etc.) post-training from libraries like HuggingFace • Utilize inference runtimes such as ONNX Runtime, vLLM for efficient execution. • Optimize batching, caching, and tensor parallelism to improve LLM scalability in real-time applications. • Develop and maintain high-performance inference pipelines using Docker, Kubernetes, and other inference servers.

🎯 Requirements

• Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, or related field. • Experience in LLM inference deployment, model optimization, and runtime engineering. • Strong expertise in LLM inference frameworks (PyTorch, ONNX Runtime, vLLM, TensorRT-LLM, DeepSpeed). • In-depth knowledge of the Python programming language for model integration and performance tuning. • Strong understanding of high-level model representations and experience implementing framework-level optimizations for Generative AI use cases • Experience with containerized AI deployments (Docker, Kubernetes, Triton Inference Server, TensorFlow Serving, TorchServe). • Strong knowledge of LLM memory optimization strategies for long-context applications. • Experience with real-time LLM applications (chatbots, code generation, retrieval-augmented generation).