I consider myself a full-stack data science leader, deep learning researcher, and machine learning systems engineer.I specialize in conceptualizing, designing and implementing gen AI/ML systems and production pipelines from scratch.I have extensive experience in optimizing across the data-model-inference lifecycle, sussing out throughput improvements via architectural optimizations ranging from ETL and data pipeline tuning to algorithmic speedups on the modeling side of things.My current tech stack is Python-focused, with the following preferred tool chains: LLMs - Sonnet 3.5, gpt4o, Llama3-8b LLM providers - Together, Groq, Anthropic, OpenAI (in that order) Embedding models - bge-small/base, gte-small/base Vector stores - pgvecto.rs, lancedb Distributed training - Modal, Ray Workflow orchestration - Modal, Airflow LLM Ops - Portkey, instructor, llamaindex, llama-cpp, and a bunch of other stuff I can’t talk about yet Classic deep learning - pytorch, huggingface, backpack Tabular ML - numpy, pandas, scikit-learn, xgboost Performance - numba, polars, RAPIDS (Dask, CuPy, CuML) Feature engineering - featuretools, tsfresh Model Serving - FastAPI, Ray ServeMy preferred data stack depends on the problem being solved, but eventually settles down into a combination of Redis, Postgres, and Parquet files on S3 (or Cloudflare R2).For MVP purposes, I have discovered that Postgres and Redis will cover most operating modes.My go-to Big Data stack defaults to Spark. I spoke at the Spark Summit on how to scale topological data analysis (a classic heavily compute-intensive task) to TBs of data on a Spark cluster by adapting locality-sensitive hashing (LSH) to tame the compute beast: https://www.databricks.com/session/enterprise-scale-topological-data-analysis-using-spark