Purpose of the job
Join our dynamic team as an Intern: AI Engineer, where you’ll be immersed in the exciting world of machine learning, deep learning, and data-driven insights. We’re looking for candidates with a passion for AI technologies who are keen to learn and grow in a fast-paced environment.
As an intern, you’ll play a key role in developing AI models, experimenting with various AI techniques, and supporting the deployment of AI solutions. You will receive training that will develop your AI engineering skills, along with exposure to the latest AI tools and frameworks.
Key Accountabilities
As an Intern: AI Engineer, you’ll be involved in:
- AI Model Development:
- Collaborate with data scientists and software engineers to design, build, and fine-tune AI models.
- Explore machine learning algorithms, including supervised, unsupervised, and reinforcement learning.
- Data Preparation and Analysis:
- Collect, clean, and pre-process data to ensure it’s ready for use in AI models.
- Conduct exploratory data analysis to derive insights and inform model development.
- Algorithm Implementation:
- Implement AI algorithms using Python libraries such as PyTorch, TensorFlow, scikit-learn, and others.
- Optimize AI models for accuracy, performance, and scalability.
- Automation and Deployment:
- Work with the engineering team to integrate AI models into production systems.
- Learn how to deploy AI models in cloud environments (e.g., AWS, Azure, Google Cloud).
- AI Research and Experimentation:
- Stay up-to-date with the latest advancements in AI research and technologies.
- Experiment with new AI techniques and contribute to ongoing AI projects.
- Visualization and Reporting:
- Use data visualization tools to present AI model outcomes and insights to stakeholders.
- Assist in creating reports that demonstrate the impact of AI on business objectives.
Skills
Programming: Proficiency in Python and familiarity with AI libraries (e.g., TensorFlow, PyTorch, scikit-learn).
Machine Learning: Knowledge of key machine learning algorithms and techniques.
Data Processing: Experience with data cleaning, preparation, and feature engineering.
Problem Solving: Ability to approach problems creatively and analytically.
Communication: Strong written and verbal communication skills for explaining technical concepts
Wish List
Experience with cloud platforms (AWS, Azure, or Google Cloud) for AI model deployment.
Familiarity with big data tools (e.g., Hadoop, Spark).
Experience with MLOps tools for automating the AI lifecycle.
Formal Education
Honours/MSc/MEng in:
Computer Science
Data Science
Mathematics
Statistics
Engineering
Artificial Intelligence
Operations Research
Behavioural (BC) |Technical (TC) |Leadership (LC)
BC_Nimble Learning
TC_Workflow Management
TC_Action Planning
TC_Policies and Procedures
BC_Manages Complexity
BC_Self-development
TC_Performance Improvement
BC_Demonstrates Self-awareness
TC_Execute and Coordinate Work
BC_Ensures Accountability
Closing Date: 10 October 2024