Ai Engineer
AI Engineer building production AI/ML for a Cape Town fintech client: personalisation, fraud detection, LLM assistants and automated workflows. Core stack is Python, ML frameworks, cloud platforms (AWS/GCP/Azure), Docker and MLOps. Hybrid role requiring 3 days per week in the Cape Town office.
- Compensation
- Not disclosed
- Location
- Cape Town, Western Cape, South Africa
- Work setup
- Hybrid
- Seniority
- Not specified
- Employment
- Full time
- Experience
- 2+ years
- Category
- Ai engineering
- Posted
Skills
Must have
- Degree in Computer Science, Machine Learning, Data Science or related technical field
- 3-5 years software engineering experience, with at least 2 years focused on AI/ML development
- Strong proficiency in Python and ML frameworks (TensorFlow, PyTorch, scikit-learn)
- Hands-on experience with cloud platforms (AWS SageMaker, Google AI Platform, Azure ML) and Docker
- Demonstrable experience building and deploying ML models in production
- Strong understanding of machine learning algorithms, deep learning architectures and neural networks
- Experience with NLP, text processing and recommendation systems
- Working knowledge of MLOps practices (model monitoring, versioning, retraining)
- Familiarity with vector databases, embedding-based search and similarity matching
- Familiarity with transformer models, large language models and modern NLP architecture
- Understanding of AI risks in a regulated environment (bias, data privacy, security)
- Experience with API development (REST, GraphQL) and microservices
- Experience with Git, CI/CD pipelines and automated testing frameworks
- Good understanding of database technologies (SQL, NoSQL) and data pipeline tools
- Knowledge of software engineering best practices, including design patterns and code quality standards
Nice to have
- Honours or Masters degree
- Experience in fintech, banking, payments or financial services
- Experience with fraud detection, risk models or credit-related use cases
- Experience with Kubernetes in production
- Understanding of web analytics, user behaviour analysis and A/B testing
- Experience with real-time streaming data processing (Kafka, Kinesis, Pub/Sub)
- Familiarity with monitoring and observability tools (Prometheus, Grafana, DataDog)
- Experience working with Jira and GitHub
Full description
AI ENGINEERFull timeLocation: Cape Town, South Africa
Hybrid: 3days per week in officeTHIS IS NOT A FULL-TIME REMOTE ROLE, ITS A HYBRID ROLE WITH CAPE TOWN OFFICE ATTENDANCE 3 DAYS A WEEK
Our client, a Cape Town based business operating in the fintech space, is focused on changing how people access and experience financial services. With ambitious growth plans ahead, they are building their AI capability and putting AI at the centre of how the business operates and serves its customers.
Main purpose of the role
Our client is looking for an experienced AI Engineer to join their team and help bring artificial intelligence into the products, platforms and processes that power the business. In this role, you'll design, build and deploy production AI solutions that add real value to customer experiences, operations and decision making.
You'll take ownership of challenging problems, from personalisation and fraud detection to LLM powered assistants and automated workflows. You'll collaborate with product owners, engineers, data specialists and business teams to deliver AI solutions that make a measurable difference for the business and its customers, working closely with the Chief Technology and Information Officer.
Key responsibilities1. AI production and delivery
- Design, develop and deploy AI/ML solutions and pipelines using cloud infrastructure (AWS, Google Cloud, Azure)
- Build and maintain production machine learning models that power features across the company's platforms and products
- Implement MLOps practices including model versioning, automated testing, monitoring and continuous deployment
- Optimise model performance, latency and cost for production environments
2. Applied AI for financial services
- Build personalisation and recommendation features that improve customer engagement and experience
- Develop models that support use cases such as fraud detection, risk assessment and customer insight
- Develop NLP solutions such as document processing, content classification, semantic search and intelligent tagging
- Integrate large language models (LLMs) and generative AI into products and internal tools, including customer service assistants, summarisation and process automation
- Work with vector databases and embedding based search to build retrieval and matching solutions
3. Responsible AI and compliance
- Apply responsible AI practices, including bias testing, explainability and appropriate human oversight
- Ensure AI solutions meet security, data privacy and POPIA requirements, as well as applicable financial services regulations