AI/ML Engineer (Hybrid NLP + Classification)

Department: Engineering

Location: Remote

Employment Type: FullTime

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 dont 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, weve 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.

Assignment Highlights

• 3-month engagement

• 6 to 10 hours per week during active phases

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

• BYOD

Key Responsibilities

- Author the AI/ML Architecture Design Document

- Recommend an accuracy threshold for the catalog mapping engine.

- Build the 4-tier catalog code mapping engine for SAP catalog types.

- 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.

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

- 100% remote work

- Payment in USD

This position is available for candidates based in LATAM.