Lead AI Engineer (LLMs & Data Pipelines)
Tasks
- Benchmark model performance across hardware
- Build LLM powered features
- Create training and evaluation data pipelines
- Define LLM evaluation metrics and quality gates
- Design embedding and retrieval pipelines
- Integrate LLM models with inference runtimes
- Integrate models into production APIs
- Optimize inference for latency cost and reliability
- Run experiments for prompts models and architectures
Perks/Benefits
Skills/Tech-stack
Edge Computing | Embedded Systems | Embeddings | Inference Optimization | Language Models | Language Processing | Large Language Models | LiteRT | MLOps | Model Evaluation | Model Monitoring | Model calibration | NLP | Natural Language | Natural Language Processing | ONNX Runtime | Python | Quality Assurance | RAG | Regression Detection | Retrieval-Augmented Generation | Semantic Search | TensorFlow Lite | Vector Database
Education
N/A
Roles
AI | AI Engineer | Engineer | Learning Engineer | Machine Learning Engineer
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