Machine Learning Data Scientist

Recruiter:

Network Recruitment

Job Ref:

NFP016133/ZG

Date posted:

Friday, October 3, 2025

Location:

Johannesburg, South Africa

Salary:

Annually

SUMMARY:
Are you passionate about pushing the boundaries of Generative AI?
We’re seeking a skilled Machine Learning Data Scientist to design, develop, and deploy next-generation AI models.

POSITION INFO:
This is your opportunity to work at the cutting edge of Generative AI, data engineering, and large-scale deployment, while collaborating with world-class teams to shape the future of intelligent systems.

Key Responsibilities:

Develop & Enhance AI Models: Create, refine, and implement Generative AI systems and Retrieval-Augmented Generation (RAG) pipelines using frameworks like LangChain and Llama-Index.
Data Engineering: Build scalable data workflows, manage ETL processes, and integrate both structured and unstructured data for AI-driven applications.
Production Deployment: Deliver models into production, collaborating with DevOps to ensure performance across cloud and on-prem environments.
Collaborate Across Teams: Work closely with engineers, product owners, and business leaders to ensure AI solutions drive measurable value.
Innovate & Research: Stay current with emerging architectures, frameworks, and methodologies, contributing to innovation and thought leadership.

Job Experience and Skills Required:

Education: Degree in Computer Science, Data Science, Machine Learning, or related field.
Experience:
- 3+ years of professional experience in AI/ML, including at least 1–2 years in Generative AI.
- Practical experience with deep learning frameworks (TensorFlow, PyTorch) and generative AI libraries.
- Familiarity with cloud platforms (AWS, GCP, Azure) and container technologies (Docker, Kubernetes).
Skills:
- Strong programming ability in Python and SQL, plus experience with libraries like Pandas and NumPy.
- Solid understanding of machine learning algorithms, neural networks, and generative models.
- Knowledge of large-scale data storage (Hadoop, Spark, Vector databases).
- Understanding of MLOps practices including model lifecycle management, monitoring, and retraining.

Apply now!

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