- Own LLM features end to end: prompts, retrieval, tool use, and APIs
- Build and tune models with classical ML, deep learning, and neural networks
- Ability to use Hugging Face models and datasets for fine-tuning, inference, and evaluation
- Deploy and run models on AWS (including Amazon Bedrock) and Azure
- Set up MLOps for training, versioning, deployment, monitoring, and rollback
- Build RAG pipelines: chunking, embeddings, retrieval, and grounding
- Measure quality with test sets, metrics, and regression checks before release
- Debug latency, cost, hallucination, and data issues in production
- Write clean, tested Python services and integrate them with existing backends