Machine Learning Engineer

1 week ago

Centurion, Gauteng, South Africa AO Connect Solutions Full time R500 000 - R1 200 000 per year

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.

Key Responsibilities1. Model Deployment & Pipeline Automation

  • 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).

2. Monitoring, Scaling & Performance

  • 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).

3. Data Integration & Governance

  • 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).

4. Documentation & Support Enablement

  • 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.

5. Continuous Improvement

  • Automate repetitive tasks (testing, retraining, rollback triggers).
  • 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.
  • Cloud
    : Azure ML stack (Azure ML, DevOps, Feature Store).
  • CI/CD
    : Git, Jenkins, Azure Pipelines.
  • Monitoring
    : Azure Monitor, Prometheus, Grafana.
  • Data Tools
    : Spark, Kafka (bonus).
  • Security
    : IAM, data governance, model audit logging.

Competencies

Competency Expectation
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 deployment success rate, rollback frequency, MTTD/MTTR.
  • Model latency, throughput, and drift over time.
  • Business value metrics linked to model performance (e.g., cost savings, conversion).

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