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

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 - Long-term projects - 100% remote work - Payment in USD - Paid Time Off (PTO) - Work from Home (WFH) & Training reimbursement - English lessons - Technical training - Career coaching This position is available for candidates based in LATAM.