Machine Learning Engineer 10027

Capitec Bank Limited; Gauteng; On-site; ZAR 500 000 - 1 200 000; Full time

Job summary

A technology solutions company is seeking a Machine Learning Engineer in South Africa, Gauteng. This role involves deploying and maintaining ML models, building automated pipelines, and ensuring model reliability. Candidates should possess a Bachelor's degree and experience in Python, with 2+ years in ML production deployments. The position offers competitive compensation and opportunities to innovate within ML practices.

Qualifications

• 2–3 years experience deploying models and building basic CI/CD pipelines.
• 4+ years experience scaling production ML and leading infrastructure design.

Responsibilities

• Translate models from notebooks to reusable, production-grade code.
• Build CI/CD pipelines for ML with automated deployment.
• Monitor live models for drift, latency, and failure.

Skills

Python

PySpark

SQL

Spark

Kafka

Education

Bachelor's degree in Computer Science, Data Science, Engineering, or similar

Master's degree preferred

Job description

Machine Learning Engineer

Centurion, Gauteng R500000 - R1200000 Y AO Connect Solutions

Posted today

Job Description

Purpose of the Role

The Machine Learning Engineer is responsible for deploying, monitoring, and maintaining ML models in production. They turn prototype models into scalable, production-grade systems by building automated pipelines, integrating with infrastructure, and ensuring data and model quality. They work closely with Data Scientists, Data Engineers, and MLOps Support to ensure models are reliable, performant, and aligned with business objectives.

Responsibilities

• Translate models from notebooks to reusable, production-grade code.
• Build CI/CD pipelines for ML (unit tests, integration tests, automated deployment).
• Manage versioning of code, data, and models (e.g., Git, DVC).
• Monitor live models for drift, latency, and failure.
• Tune models and pipelines for performance and cost-efficiency.
• Implement load testing and alerting (Prometheus, Grafana, Azure Monitor).
• Collaborate with Data Engineers to manage feature pipelines and real-time data flow.
• Ensure training/inference data meets governance and compliance requirements.
• Implement Feature Store solutions where relevant (e.g., Azure Feature Store).
• Provide clear documentation for handover to MLOps support.
• Define IAM roles and controls for model access across dev/test/prod.
• Lead training or walkthroughs for deployment best practices.
• Introduce modern techniques like streaming inference, canary deployments, or serverless ML.
• Participate in post-mortems and incident reviews to strengthen MLOps maturity.

Required Skills & Experience

Education

• Bachelor's degree in Computer Science, Data Science, Engineering, or similar.
• Master's degree preferred.

Experience

Intermediate

• 2–3 yrs Deploy models, build basic CI/CD, script pipelines

Senior

• 4+ yrs Scale production ML, lead infra design, mentor others

Technical Skills

• Languages: Python (required), PySpark, SQL.
• Data Tools: Spark, Kafka (bonus).

Competency Expectations

• Problem Solving: Debug and optimise model pipelines; fix deployment failures
• Innovation: Automate, optimise, and introduce emerging MLOps practices
• Communication: Explain infra to both technical and non-technical stakeholders
• Teamwork: Collaborate across DS, DE, and Support; mentor juniors
• Change Advocacy: Champion new tools, frameworks, or practices in ML lifecycle

Performance Metrics

• Model latency, throughput, and drift over time.
• Business value metrics linked to model performance (e.g., cost savings, conversion).

Is this job a match or a miss?

This advertiser has chosen not to accept applicants from your region.

3