Production AI Engineer — FinTech (Cape Town)

Liquid Thought Group; Cape Town; On-site; ZAR 700,000 - 1,000,000; Full time

Benefits offered by this job

Competitive salary

High impact work

Real ownership

Growth opportunities

Collaborative environment

Job summary

Liquid Thought Group in Cape Town, SA, is seeking an Intermediate AI Engineer to design, build and deploy production AI solutions across fintech platforms. You will own ML pipelines, integrate LLMs and NLP features, and collaborate with product, data, and engineering teams to deliver measurable business value.

Responsibilities include production model deployment, monitoring, and optimisation, while upholding responsible AI, data privacy, security and governance standards.

Qualifications

• Degree in Computer Science, ML, Data Science or related field.
• 3–5 years software engineering, with at least 2 years focused on AI/ML development.
• Strong Python and ML frameworks (TensorFlow, PyTorch, scikit-learn).
• Hands-on with cloud platforms (AWS SageMaker, Google AI Platform, Azure ML) and Docker.
• Experience deploying ML models in production.

Responsibilities

• Design, build and deploy production AI solutions across products and platforms.
• Own ML pipelines and model deployment, monitoring and optimization.
• Develop NLP, recommendations, fraud detection and intelligent tagging features.
• Collaborate with product, data and business teams to deliver AI solutions.
• Ensure responsible AI, security, data privacy and governance.
• Contribute to architecture discussions and code reviews.

Skills

Python

ML frameworks

Cloud platforms

MLOps

NLP

LLMs

Vector search

Transformer models

Code reviews

Git & CI/CD

Education

Computer Science / ML / Data Science degree

Tools

TensorFlow

PyTorch

scikit-learn

AWS SageMaker

Docker

Kubernetes

REST/GraphQL

Job description

Liquid Thought Group in Cape Town, SA, is seeking an Intermediate AI Engineer to design, build and deploy production AI solutions across fintech platforms. You will own ML pipelines, integrate LLMs and NLP features, and collaborate with product, data, and engineering teams to deliver measurable business value.

Responsibilities include production model deployment, monitoring, and optimisation, while upholding responsible AI, data privacy, security and governance standards.