Applied AI Engineer
Tasks
- Build RAG and agent orchestration
- Build data collection labeling versioning pipelines
- Create model evaluation harnesses
- Deploy models across APIs self hosted and edge
- Develop real time streaming voice pipelines
- Fine tune and retrain models with LoRA
- Integrate ML components with signal processing and computer vision pipelines
- Integrate foundation models into production pipelines
Perks/Benefits
- N/A
Skills/Tech-stack
AWS SageMaker | Active Learning | Agent Orchestration | Azure Machine Learning | Computer Vision | DVC | Data Processing | Edge ML | Edge ML deployment | Embeddings | Fine Tuning | Human-in-the-loop | Language Models | Large Language Models | Llama CPP | LoRA | ML deployment | MLflow | Machine Learning | ONNX | PEFT | Python | Quantization | RAG | Retrieval-Augmented Generation | Speech Recognition | Streaming Data | Streaming data processing | Supervised Fine Tuning | TensorRT | Text to Speech | The Loop | VLLM | Vertex AI | Vision Language Models | Vision-language | Weights and Biases
Education
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