Job Description
Cape Town – Western Cape – South Africa, Remote
- Take models from Data Scientists (notebooks, prototypes) and productionize them into scalable APIs and pipelines.
- Build CI/CD pipelines for ML: automated testing, validation, deployment, rollback.
- Implement monitoring for data drift, model drift, and performance decay with automated alerts and retraining triggers.
- Maintain reproducible environments for training and inference (Docker, SageMaker, Lambda, Step Functions).
- Design AWS-native ML infrastructure optimized for cost and scale (ECS/EKS, SageMaker, Lambda, Glue, Step Functions, S3).
- Build ETL/ELT pipelines that prepare structured and nested JSON data from PostgreSQL (BI reporting) and other sources.
- Ensure models integrate seamlessly into real-time decisioning engines.
- Collaborate with Backend Engineers (Node.js/JavaScript) to integrate ML services into production systems.
- Build microservices & APIs for inference, feature engineering, and data transformations.
- Ensure low-latency, fault-tolerant services for real-time lending decisions.
- Partner closely with Data Scientists to understand models, features, and assumptions.
- Work with Software Engineers to ensure production systems can consume models efficiently.
- Act as the bridge between research and Engineering, ensuring models dont get stuck in notebooks.
- Postgraduate Degree in a numerate discipline such as Statistics, Mathematics, Software Engineering, Computer Science, or a related field.
- Relevant AWS Certifications (e.g., AWS Certified Machine Learning Specialty, AWS Solutions Architect).
- MLOps-related Certifications or professional courses (e.g., Coursera, Udacity, or equivalent).
- 3+ Years experience in ML Engineering, Data Engineering, or Software Engineering with focus on ML deployment.
- Proven track record of deploying ML models into production (SageMaker, Lambda, Step Functions, or equivalent).
- Experience building CI/CD pipelines for ML.
- Strong Backend/Service Development skills (Node.js, Python, or JavaScript).
- Deep experience with AWS infrastructure (EC2, ECS/EKS, S3, Lambda, Glue, Step Functions).
- Strong SQL + PostgreSQL skills, including working with deeply nested JSON data.
- Experience in FinTech, Credit, or Risk Modelling.
- Understanding of multi-agent AI systems and advanced Feature Engineering (e.g., NLP on bank statements, credit bureau data).
- Cost-optimization experience on AWS.
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