ML Engineer / AI Platform Lead
Tasks
- Assemble context for large token window
- Build retrieval augmented generation pipeline
- Collect user feedback and generate training data candidates
- Create per tenant system prompts and task routing
- Deploy and optimize large language model for production inference
- Design tenant scoped LoRA training workflow
- Evaluate model performance and run A B testing
- Generate embeddings and store in vector database
- Implement cross encoder reranking
- Implement defensive structured JSON output parsing
- Isolate deployment environments
- Iterate prompts with domain expert
- Monitor inference quality hallucination and citation accuracy
- Track latency and parsing failure rates
Perks/Benefits
- N/A
Skills/Tech-stack
A/B | A/B Testing | B testing | Continuous batching | Cross Encoder Reranking | Cross-Encoder | Document chunking | Embeddings | FastAPI | Few-Shot Learning | Few-shot | Fine Tuning | JSON | LoRA | Low Latency | Low-latency serving | Model Evaluation | Model Monitoring | Output parsing | Prompt engineering | PyTorch | Python | QLoRA | Quantization | Retrieval-Augmented Generation | Structured Output | Structured output parsing | System prompts | Task routing | Text Generation | Text generation inference | Transformers | VLLM | Vector Databases
Education
N/A
Roles
AI | AI Engineer | Engineer | ML Engineer
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