Alternate Job Titles
Intelligence Manager, Business Intelligence Manager, Intelligence Specialist, AI Manager
Job Level
Specialist
Functional Group
Software System and Analytics
Job Family
Development and Deployment
Job Description
- Design, develop, deploy and maintain artificial intelligence (AI) systems powered by machine learning (ML) to address digital challenges across the organisation.
- Develop AI systems using techniques such as ML, Natural Language Processing (NLP), rule-based logic, and fuzzy logic.
- Implement suitable machine learning algorithms, conduct tests, and keep up with industry advancements.
- Build data models, conduct statistical analysis, and retrain systems to maximise efficiency.
- Create effective self-learning apps and contribute to advancements in AI technologies.
- Manage initiatives aimed at producing AI models that are optimised and scalable and makes sure that the right parties are informed and working together.
- Manage the end-to-end validation of AI models, ensuring the chosen methodologies and algorithms meet functional requirements and performance standards.
- Lead and develop the AI team’s technical capabilities, providing expert guidance and coaching to achieve excellence across all project domains.
- Manage data for collection and processing workflow to ensure high quality inputs for the AI system.
- Oversee and direct the AI development team, fostering a collaborative environment and leveraging the team’s collective expertise in programming and statistical modelling to execute projects that meet business needs.
- Manage and cultivate relationships with stakeholders (internal and external), translating technical AI concepts into clear business value and influencing decision-making.
- Ensure the AI systems were developed and implemented in alignment with ethical principles and organizational values.
Critical Work Function
ML Algorithm and Model Research
- Research and apply machine learning (ML) tools and algorithms for model development.
- Identify suitable ML algorithms based on business or user requirements.
- Select and prepare appropriate datasets and data representation techniques for ML analysis.
- Evaluate and validate ML models to ensure performance and reliability prior to deployment.
AI Model Construction and Evaluation
- Write code to bundle the ML and AI models for scalability.
- Develop infrastructure and pipelines to support AI model development.
- Build scalable data pipelines to load, integrate, extract, and process unstructured data from multiple sources.
- Optimise AI models for production environments and large-scale implementation.
- Support ongoing innovation and advancements in artificial intelligence technologies.
Production-Ready AI Model Development
- Assess packing codes and model scaling for AI refinement.
- Evaluate the scalability of production-level AI model performance.
- Oversee the infrastructure and pipeline for AI development.
- Oversee the loading, integrating, extracting, and transforming of unstructured data in preparation.
AI Model Implementation
- Manage the deployment and integration of AI technologies.
- Utilise tagged data to train and optimise machine learning models for accurate and reliable performance.
- Develop a post-deployment test plan.
- Communicate deployment issues and proposed resolutions to stakeholders.
- Lead the design and implementation of supervised and/or unsupervised AI problem solving techniques.
AI Initiative Management
- Coordinate end-to-end implementation of AI solutions, including initial testing, deployment, and optimisation of runtime and system performance.
- Oversee code reviews and project estimations.
- Establish work quality standards and project schedules.
- Apply project management procedures and tools to ensure the projects run successfully within time, budget and quality expected.
- Communicate project objectives at critical milestones to secure stakeholder alignment.
- Create production-ready AI Model with a focus on scalability, performance and maintainability.
Entry Requirements
#1
Artificial Intelligence Engineer
BDQF Level 6 in Artificial Intelligence, Computer Science, or any related field, with related industry certification, and a minimum of 5 years related working experience or
BDQF Level 5 in Artificial Intelligence, Computer Science, Information Systems, or any related field, with relevant industry certification or portfolio, and a minimum of 8 years related working experience.
Skills & Competencies
Technical Skills
Soft Skills
Recommended Technical Training Courses
Amazon Web Services (AWS) Certified Data Analytics – Specialty
Cloudera Data Platform Generalist Certification
Data Science Council of America (DASCA) Associate Big Data Engineer
Data Science Council of America (DASCA) Senior Big Data Engineer
Google Professional Data Engineer
IBM Certified Solution Architect – Data Warehouse V1
IBM Certified Data Architect – Big Data
SAS Certified Data Integration Developer
Enterprise Big Data Architect