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

🌍 Remote, USA 🎯 Full-time 🕐 Posted Recently

Job Description

Hi there :)

Thanks for checking in to find out about our open position. We´ll provide as much information as possible, but please feel free to reach us if you have further questions. We´ll be happy to see your application, even if there are skills you don't quite master!

About Us

At South Geeks, we engage top-performing Software Engineers, Security Experts, and Data Analysts from Latin America to join our clients' teams worldwide. For over 8 years, we've been helping future-shaping companies scale faster by curating world-class tech talent and building long-lasting, strategic partnerships. We pride ourselves on a people-centered culture that powers innovation, collaboration, and excellence.

About the Client

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. The work runs in 8 Agile-Scrum sprints over 16 weeks, fully remote, with a small senior team operating end to end.

About the Role

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.

Assignment Highlights

4-month engagement (16 weeks total; active in W1 to W10)

6 to 10 hours per week during active phases; 60 hours total

100% remote, Eastern Time overlap for gate and sprint reviews

BYOD

Key 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.

We recommend a hybrid architecture: a deterministic rule-based layer plus a supervised ML classifier with human-in-the-loop oversight. A pure rule-based fallback is acceptable.

What You Need to Succeed in This Role

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 have: precision/recall trade-off framing for catalog mapping; SAP catalog code domain knowledge.

Our Team

We strive to create an inspiring and growth-oriented environment where everyone feels valued, heard, and empowered. We promote both personal and professional development, with individualized support for your needs and concerns. We aim to build a space where everyone can thrive.

What We Offer