model selection , Information Retrieval, Deep Learning, Scalability, Nlp, offline evaluation, failure recovery, LLMs, Indexing, distributed-data systems, schema evolution, large-scale processing, data and feature strategy, Monitoring, classical ML, model or data drift, document understanding, applied machine learning, production ML infrastructure, ML-powered Search, observability, content representations

Jax, Cloud Infrastructure, Pytorch, Distributed Systems, Python, TensorRT-LLM, GPU infrastructure, Vector databases, SGLang, vLLM, Workflow orchestration, ML data pipelines, Performance tooling, Retrieval infrastructure

Git, Agile Scrum, Apache Spark, Python, Generative AI, AWS Bedrock, RAG architectures