Artificial Intelligence/Machine Learning Engineer
The Artificial Intelligence /Machine Learning Engineer supports the production of scalable and optimised artificial intelligence (AI)/machine learning (ML) models. He/She focuses on building algorithms for the extraction, transformation and loading of large volumes of real-time, unstructured data in order to deploy AI/ML solutions from theoretical data science models. He runs experiments to test the performance of deployed models, and identifies and resolves bugs that arise in the process. He works in a team setting and is proficient in statistics, scripting and programming languages required by the organisation. He is also familiar with the relevant software platforms in which the models are deployed. He should be knowledgeable of the requirements under the Model AI Governance Framework and the Personal Data Protection Act (PDPA) in the course of his work on AI/ML models. The AI/ML Engineer is a determined individual who is comfortable working with large data sets, has a keen interest in problem solving and experimentation, and enjoys the iterative process of development and resolving issues.
What Does a Artificial Intelligence/Machine Learning Engineer Do?
Key Responsibilities & Tasks
Conduct research on artificial intelligence (AI)/machine learning (ML) models and algorithms
- Research and implement ML algorithms and tools for AI/ML model development
- Identify appropriate algorithms based on user requirements
- Select appropriate datasets and data representation methods for analysis
- Evaluate AI/ML models for production
Build and assess AI/ML models
- Develop codes to package the AI/ML models for scaling
- Develop AI/ML development pipeline and infrastructure
- Develop scalable data pipelines to extract, transform, load and integrate unstructured data from various sources
- Scale AI/ML models for production
- Support continuous improvement of AI solutions
Deploy AI/ML models in solutions
- Test the operation and performance of the deployed models
- Identify bugs during deployment and create bug fixes to address issues
- Engage in code reviews to improve AI/ML models
- Perform statistical analysis and fine tuning of the models using test results
- Prepare documentation to outline data sources, models and algorithms used and developed
- Research and implement machine learning algorithms and tools for AI/ML model development
Do You Have the Skills for This Role?
A Artificial Intelligence/Machine Learning Engineer needs 3 core competencies. Here's what's required and at what level.
Supporting Skills
Developing People
IntermediateInteracting with Others
Transdisciplinary Thinking
IntermediateThinking Critically
Communication
IntermediateInteracting with Others
SkillsFuture Skill Levels
3 levelsBasic
Recognise and understand fundamental concepts. Apply skills in routine situations with guidance.
Intermediate
Apply skills in varied situations independently. Analyse problems and adapt approaches as needed.
Advanced
Lead and innovate in complex situations. Evaluate strategies, guide teams, and drive improvements.
Technical Skills & Competencies (TSC) Levels
6 levelsFollow
Carry out routine tasks under close supervision. Follow established procedures and guidelines.
Assist
Perform tasks with some independence. Assist in non-routine situations and apply established techniques.
Apply
Apply skills and knowledge independently in varied situations. Analyse problems and adapt approaches.
Analyse
Analyse complex situations and develop solutions. Guide and mentor junior colleagues.
Strategise
Set strategic direction and drive innovation. Evaluate trade-offs and make high-impact decisions.
Transform
Lead industry transformation. Establish standards, shape policy, and provide expert advisory.
Technical Skills & Competencies
A Artificial Intelligence/Machine Learning Engineer requires 20 technical skills at specific proficiency levels.
Business Needs Analysis
Level 4Business and Project Management
Investigate existing business processes, evaluate requirements and define the scope for recommended solutions and programmes
Computer Vision Technology
Level 4Development and Implementation
Set-up and deploy video analytics algorithms and perform system performance evaluations
Data Design
Level 4Design and Architecture
Design data models and data flow diagrams and mechanisms to optimise the flow, maintenance, storage and retrieval of data
Data Governance
Level 4Governance and Compliance
Implement guidelines, laws, statutes and regulations on appropriate handling of data at various stages in their lifecycle, and monitor compliance with data policies
Data Strategy
Level 4Strategy Planning and Implementation
Develop data management structures and recommend policies, processes and tools for effective data storage, handling and utilisation
Emerging Technology Synthesis
Level 4Business and Project Management
Evaluate new and emerging technology and trends against the organisational needs and processes
Intelligent Reasoning
Level 4Development and Implementation
Build knowledge-based intelligent software applications using machine reasoning techniques and computer programming
Pattern Recognition Systems
Level 4Development and Implementation
Analyse data by deriving useful hidden patterns in the data, select and apply the most suitable pattern recognition techniques to solve problems and develop pattern recognition systems
Project Management
Level 4Business and Project Management
Plan and drive medium scale projects or programmes, including allocating resources to different parts, and engaging stakeholders on the project's progress and outcomes
Self-Learning Systems
Level 4Development and Implementation
Plan the end-to-end process to design, build and deploy adaptive software robots in hardware and devices, validating and optimising software robots in different application areas
Stakeholder Management
Level 4Stakeholder and Contract Management
Develop a stakeholder engagement plan and negotiate with stakeholders to arrive at mutually-beneficial arrangements
Text Analytics and Processing
Level 4Development and Implementation
Analyse text data to discover themes, patterns and trends to improve business processes and decision making
Cloud Computing
Level 3Development and Implementation
Deploy cloud solutions and resolve cloud integration issues
Computational Modelling
Level 3Development and Implementation
Identify and utilise appropriate statistical algorithms and data models to test hypotheses and derive patterns or solutions
Configuration Tracking
Level 3Development and Implementation
Develop and update a configuration management plan, determining systems and techniques to track changes and revisions
Data Engineering
Level 3Development and Implementation
Implement data management processes and systems to map data sources, processes and relationships, and transform and process multiple streams of data
Database Administration
Level 3Operations and User Support
Monitor and maintain databases, and troubleshoot database errors faced, and ensure appropriate levels of user access to databases
Security Architecture
Level 3Design and Architecture
Design secure systems and define security specifications of components, integrating appropriate security controls
System Integration
Level 3Development and Implementation
Perform basic compatibility assessments and integrate selected system components according to a plan
Test Planning
Level 3Development and Implementation
Determine requirements and develop a phase test plan, identifying optimal schedules and means for executing test scripts
European Skills Framework
ESCOSkills and knowledge areas required for this occupation based on European classification.
Essential
Optional
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