Senior Specialist Machine Learning Engineer

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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: 30 July 2026.

The base location for this role is Midrand, Vodacom Campus.

The company's approved Employment Equity Plan and Targets will be considered as part of the recruitment process. As an Equal Opportunities employer, we actively encourage and welcome people with various disabilities to apply.
Vodacom is committed to an organisational culture that recognises, appreciates, and values diversity & inclusion.