- Develop and improve NLP systems and language model-powered experiences.
- Fine-tune and optimize open-source and proprietary language models for domain-specific use cases.
- Design and implement evaluation frameworks to measure model quality, reliability, and business impact.
- Build automated testing pipelines to identify regressions, quality drops, hallucinations, and failure modes.
- Develop metrics and monitoring systems to continuously assess model performance in production.
- Design and implement guardrails that improve model reliability, safety, and consistency.
- Build systems to detect unsafe outputs, prompt injection attempts, abuse patterns, and fraud-related behaviors.
- Optimize inference performance through quantization, distillation, batching, and model serving improvements.
- Deploy and maintain production-grade ML systems running on GPU infrastructure.
PythonSQLMachine Learning+5 more