Engineer/ Senior Engineer/ Principal Engineer (AI Engineering), Digital Hub

About the Role

We are looking for an individual to join us in our Digital Hub Programme Centre where you will participate in AI Engineering initiatives. The role will require you to execute the AI Engineering roadmap through research, experimentation and implementation of:

• MLOps (CI/CD/CT) pipelines and tools
• Supporting infrastructure for AI development and deployment, and
• Governing processes and release criteria

Some examples of your work could include:

• Design and build pipelining tools and processes to automate the MLOps process
• Research, design and build ML-specific testing and remediation techniques (E.g. Unit tests, Robustness tests) for the AI community
• Research, design, develop and optimize domain specific ML deployment, monitoring and retraining techniques for the AI community

Requirements

• Tertiary qualification in Computer Science, Information Systems, Computer Engineering, or related fields
• 1 year of experience in MLOps domain/ area preferred.
• Excited to gain knowledge in a new domain
• Team player with good communication skills
• Passionate and self-motivated

Required Skills

• ML development
  • Basic ML tasks (E.g. Object Detection as a ML CV task).
  • Modular coding for Machine Learning (Pipelines)
  • Data preprocessing and ML model training using any ML frameworks
• Software Engineering and Infrastructure
  • Programming: Python
  • Scripting: Bash
  • Linux Operating Systems (E.g. Debian, RHEL)
  • Version control (E.g. git)
  • Containerization and Container Orchestration (E.g. Docker, Kubernetes.)

Preferred Skills (Previous experience would be advantageous)

• MLOps
  • Data Versioning
  • Experiment Orchestration (E.g. MLOps E2E Tools)
  • Model Versioning
  • Model Serving (E.g. Inference engines)
  • Unit Testing (E.g. Directional Expectation Tests, Invariance Testing)
  • Robustness Testing (E.g. Adversarial AI, Brittleness, Explainability)
  • Model/Data Monitoring pipelines
  • Model retraining pipelines
  • Labeling (E.g. multi-type labelling tools)
• Cloud Infrastructure
  • Hyperconverged Infrastructure (HCI)
  • Networking
  • Storage (E.g. S3)
• DevOps
  • Automation (E.g. Jenkins)
  • Monitoring dashboards (E.g. Prometheus/Grafana.)
  • Messaging (E.g. Kafka.)
  • RESTful web services (E.g. https, gRPC)