ML Engineer / AI Platform Lead
Tasks
- Build and optimize RAG pipeline
- Build context layer with prompt routing
- Deploy and optimize large language model inference
- Design feedback collection pipeline for training data
- Evaluate model quality with A B testing
- Implement defensive structured output parsing
- Implement tenant scoped LoRA training workflow
- Iterate prompts with domain experts
- Monitor inference quality and latency per tenant
Perks/Benefits
Skills/Tech-stack
Continuous batching | Cross Encoder Reranking | Cross-Encoder | Document chunking | Embeddings | FastAPI | Few-Shot Learning | Few-shot | JSON | Language Models | Large Language Models | LoRA | Low Latency | Machine Learning | Model Serving | Output parsing | Prompt engineering | PyTorch | Python | QLoRA | Quantization | Retrieval-Augmented Generation | Structured Output | Structured output parsing | System prompts | Text Generation | Text generation inference | Transformers | VLLM | Vector Databases
Education
N/A
Roles
AI | AI Engineer | Engineer | Learning Engineer | Machine Learning Engineer
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