Machine Learning Engineer (Model Deployment or MLOps) (CPT Hybrid or Remote)

DataFin Recruitment; Remote; ZAR 600 000 - 800 000; Full time

Job summary

A forward-thinking fintech firm in Cape Town seeks a passionate Machine Learning Engineer focusing on MLOps to optimize lending decisions. The position requires a postgraduate degree in a numerate field and over three years of experience in ML-related roles, with a strong emphasis on deploying models into production. Candidates should have expertise in AWS infrastructure and backend development. Join a dynamic team where your innovations can directly impact financial solutions.

Qualifications

• 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.
• Experience building CI or CD pipelines for ML.

Responsibilities

• Take models from Data Scientists and productionize them into scalable APIs and pipelines.
• Build CI or CD pipelines for ML with automated testing and validation.
• Implement monitoring for data and model drift with automated alerts.

Skills

MLOps

SQL

PostgreSQL

Node.js

Python

JavaScript

AWS infrastructure

Education

Postgraduate Degree in Statistics, Mathematics, Software Engineering, or related field

Relevant AWS Certifications

MLOps-related Certifications

Tools

SageMaker

Lambda

Step Functions

Docker

Glue

Job description

ENVIRONMENT

A fast-paced FinTech company seeks a passionate Machine Learning Engineer (MLOps focus) to power instant lending decisions - no humans in the loop. Its models drive credit risk, portfolio management, and lifecycle decisioning with the biggest challenge being moving models from Data Science into reliable production systems. They’re looking for you to bridge that gap and ensure that every model built makes it into production—fast, reliable, and cost-efficient. The ideal candidate will require a Postgraduate Degree in a numerate discipline such as Statistics or Mathematics or Software Engineering or a related field with 3+ years’ experience in ML Engineering, Data Engineering, or Software Engineering with focus on ML deployment. You also need a proven track record of deploying ML models into production (SageMaker, Lambda, Step Functions, or equivalent), strong SQL, PostgreSQL, Node.js, Python, or JavaScript & AWS infrastructure (EC2, ECS or EKS, S3, Lambda, Glue, Step Functions).

DUTIES

Model Deployment & MLOps

• Take models from Data Scientists (notebooks, prototypes) and productionize them into scalable APIs and pipelines.
• Build CI or 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).

Infrastructure & Pipelines

• Design AWS-native ML infrastructure optimized for cost and scale (ECS or EKS, SageMaker, Lambda, Glue, Step Functions, S3).
• Build ETL or 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.

Integration & APIs

• Collaborate with Backend Engineers (Node.js or 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.

Collaboration

• 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 don’t get stuck in notebooks.

REQUIREMENTS

Qualifications

• 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).

MUST-HAVEs

• 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 or CD pipelines for ML.
• Strong Backend or Service Development skills (Node.js, Python, or JavaScript).
• Deep experience with AWS infrastructure (EC2, ECS or EKS, S3, Lambda, Glue, Step Functions).
• Strong SQL + PostgreSQL skills, including working with deeply nested JSON data.

Nice-to-have skills

• 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.

While we would really like to respond to every application, should you not be contacted for this position within 10 working days please consider your application unsuccessful.