NLP Data POC Pipeline Build

I’m assembling a proof-of-concept that blends natural-language processing, data discovery and rigorous statistical modelling into one demonstrable pipeline, and I want to partner with a Pune-based freelancer who can own the build from start to finish.

The core tech stack is Python + Git, and I expect a true prototyping mindset: quick iterations, clear commits and the confidence to rip out code that no longer serves the goal. On the NLP side you’ll wire up spaCy for classic linguistic tasks while also tapping LLM APIs for higher-level comprehension. For data engineering we must pull table metadata via SQL, harvest schemas, and feed both structured data and free text into an embeddings layer that drives semantic search; sqlalchemy and psycopg2 are the current connectors in play.

Once the data is flowing, we move into stats: hypothesis tests, diagnostics and regression models written in scikit-learn or statsmodels, whichever is more appropriate for the slice of analysis. The entire flow should surface through lightweight JSON APIs, orchestrated end-to-end and containerised via Conda or Docker so I can spin it up on any dev box without surprises.

Deliverables
• Clean Git repository with modular, commented code
• Dockerfile or environment.yml proving easy setup
• Sample dataset plus schema-extraction script
• Working REST endpoints that trigger:
– spaCy + LLM inference
– semantic search over embeddings
– statistical analysis and returned metrics
• One-page runbook explaining how to extend or swap components
• Short screen-capture demo showing the pipeline in action

I’ll evaluate success strictly on reproducibility (fresh clone → deploy → same results), clarity of code, and the smooth hand-off of knowledge. If you’re in Pune, fluent in Python, and excited to stitch together spaCy, LLM APIs, SQL harvesting and robust stats, let’s make this demo sing.