Edge LLM Engineer

Job Summary

We are looking for an engineer experienced in deploying and optimizing Large Language Models on edge devices, preferably NVIDIA Jetson platforms. The role is not limited to model inference; the candidate should be able to design practical LLM-based solutions for real-world scenarios using prompt engineering, input preprocessing, caching strategies, data creation, and model fine-tuning when required. The ideal candidate should understand how to make LLM applications reliable, efficient, and context-aware under edge-device constraints such as limited compute, memory, latency, and power.

Responsibilities

Design practical LLM-based solutions for real-world scenarios using prompt engineering, input preprocessing, caching strategies, data creation, and model fine-tuning when required. Understand how to make LLM applications reliable, efficient, and context-aware under edge-device constraints such as limited compute, memory, latency, and power.

Mandatory Skills

• Hands-on experience with LLMs, prompt engineering, and scenario-specific prompt design.

• Experience running AI/ML models on edge devices with compute and memory constraints.

• Practical knowledge of preprocessing techniques for text, speech transcripts, and structured inputs.

• Experience implementing caching, context management, and optimization techniques for LLM applications.

• Ability to create datasets and fine-tune or adapt models for domain-specific use cases.

• Strong Python programming skills.

• Understanding of NLP tasks such as intent handling, entity extraction, and text classification.

• Experience with model evaluation, latency optimization, and debugging AI behavior.

• Familiarity with NVIDIA Jetson or similar edge AI platforms.

  Good to Have Skills

• Experience with speech processing, speech-to-text systems, and audio preprocessing.

• Knowledge of noise handling, speech enhancement, and robust voice input pipelines.

• Experience with NER models and entity extraction pipelines.

• Familiarity with TensorRT, ONNX, PyTorch, Hugging Face, or similar model deployment tools.

• Experience with quantization, pruning, distillation, or other model compression techniques.

• Knowledge of retrieval-augmented generation, vector databases, or local knowledge caching.

• Experience building real-time AI applications on embedded Linux systems.

• Familiarity with multilingual or domain-specific language processing.

• Experience integrating LLMs with sensors, robotics, industrial systems, or IoT devices.

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