Senior Specialist Machine Learning at Vodafone Global Enterprise

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:

Education:

• 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).

Experience:

• 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. 

Preferred Certifications

• AWS Certified Machine Learning – Specialty
• Databricks Certified ML Professional
• Kubernetes Administrator (CKA)
• MLflow or MLOps specialization (e.g., DeepLearning.AI, Coursera)

Core competencies, knowledge, and experience:

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.

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: 06 April 2025.