AI/ML Engineer (Part-Time, Hybrid NLP + Classification)

South Geeks•Wyoming, NY

4d•Hybrid

About The Position

We are looking for a part-time AI/ML Engineer to design and build the AI component of the platform: a hybrid rule-based plus ML system that maps contractor free-text into a 4-tier SAP catalog hierarchy, flags anomalies, and learns from operator corrections. This is a 4-month engagement and your active participation spans SP0 through SP3 (W1 to W10), at roughly 6 to 10 hours per week during active phases. The work runs in 8 Agile-Scrum sprints over 16 weeks, fully remote, with a small senior team operating end to end. Our client is a Fortune 500 global energy company running a strategic initiative to migrate historical Plant Maintenance data into SAP S/4HANA through a governed, auditable web application integrated with SAP CPI.

Requirements

NLP / text classification in Python (scikit-learn or HuggingFace transformers).
AI architecture documentation: a clean model design doc readable by non-technical reviewers.
Rule-based NLP (keyword extraction, regex, scoring logic).
ML classification pipeline (training data preparation, evaluation, tuning).
Anomaly detection in tabular data.
Python data science stack (pandas, numpy, scikit-learn, optionally spaCy).
Model feedback loop design.
API / microservice wrapping of an ML model.

Nice To Haves

precision/recall trade-off framing for catalog mapping
SAP catalog code domain knowledge

Responsibilities

Author the AI/ML Architecture Design Document at Gate 0 (W2). This is the W2 blocker for the whole project and must be readable by a non-technical PM.
Recommend an accuracy threshold for the catalog mapping engine.
Build the 4-tier catalog code mapping engine for SAP catalog types (Object Part B, Symptom C, Cause 5, Activity A).
Implement the AI anomaly detection module.
Design and implement the "Train Model" feedback loop where operator corrections retrain the model.
Wrap the model as a microservice for the main app.