Role Purpose/Business Unit:
- As a Senior Machine Learning Engineer, you will play a critical role in designing, deploying, and operationalizing scalable machine learning solutions across multiple markets.
- You will work with cross-functional teams including data scientists, data engineers, software engineers, and product managers to integrate models into production with a robust MLOps lifecycle.
- This role demands strong experience in ML engineering at scale, advanced automation, real-time model serving, and compliance-aware development.
Your responsibilities will include:
- ML System Design & Architecture: Architect and implement scalable, secure ML pipelines using tools like MLflow, SageMaker, or Databricks. Design reusable templates for batch and real-time inference.
- Production Model Deployment: Automate deployment of models into production with CI/CD, containerization Docker, orchestration Kubernetes, and feature stores.
- Model Monitoring & Governance: Implement real-time model monitoring, drift detection, and lineage tracking to ensure model performance and compliance.
- Collaboration & Delivery: Partner with data scientists to translate prototypes into production-ready code. Engage with software and data engineering teams to integrate models into larger systems and APIs.
- Automation & Testing: Enforce test-driven development for model code. Implement automated tests for pipelines, accuracy thresholds, and data validation.
- Documentation & Knowledge Sharing: Maintain clear technical documentation for models, pipelines, and workflows. Lead internal workshops on MLOps best practices and tooling.
- Mentorship: Mentor junior MLEs and data scientists on engineering best practices, reproducibility, and scalable ML system design.
The ideal candidate for this role will have:
- Bachelor’s degree in Computer Science, Data Science, Engineering, Applied Mathematics, or a related field.
- Master’s degree or PhD preferred especially with specialization in Machine Learning, AI, or Distributed Systems.
- 8+ years in data science or ML engineering roles, including at least 5 years in production-grade ML deployment and operationalization.
- Hands-on experience with distributed computing frameworks and model orchestration at enterprise scale.
Technical Skills
- Languages: Python mandatory, Bash, SQL; optional: Scala or Java.
- ML Libraries & Frameworks: Scikit-learn, XGBoost, TensorFlow, PyTorch.
- MLOps & Model Lifecycle: MLflow, SageMaker, Databricks, Kubeflow, Airflow.
- Infrastructure: AWS EC2, S3, SageMaker, IAM, EKS, Terraform, Kubernetes, Docker.
- Feature Engineering: Feature Stores e.g., SageMaker Feature Store, Feast.
- Monitoring: Prometheus, Grafana, CloudWatch, Evidently AI.
- Security & Compliance: RBAC, KMS, encryption, reproducibility, model versioning.
Soft Skills
- Strategic problem-solver with strong system thinking.
- Excellent communication and technical storytelling across technical and business stakeholders.
- Comfortable working in agile squads and fast-paced delivery environments.
- Passionate about enabling ML at scale and setting engineering standards.
Preferred Certifications
- AWS Certified Machine Learning – Specialty
- Databricks Certified ML Professional
- Kubernetes Administrator CKA
- MLflow or MLOps specialization e.g., DeepLearning.AI, Coursera
We make an impact by offering:
- Enticing incentive programs and competitive benefit packages
- Retirement funds, risk benefits, and medical aid benefits
- Cell phone and data benefits, advantages fibre connection discounts, and exclusive staff discounts offered in collaboration with partner companies
Closing date for Applications: 02 July 2026.
