Senior Artificial Intelligence/Machine Learning Engineer
The Senior Artificial Intelligence/Machine Learning Engineer oversees projects for the production of scalable and optimised artificial intelligence (AI)/machine learning (ML) models, and ensures communication and collaboration with appropriate stakeholders. He/She is responsible for evaluating techniques or algorithms used and ensuring performance of the models deployed, and guiding or coaching the team in achieving quality in technical areas of the projects. 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 on which the solution is deployed on. The Senior AI/Machine Learning Engineer is passionate about delivering end-to-end data projects, from scoping and discovery to post-deployment. He is skilled in stakeholder management, possesses the ability to communicate ideas and recommendations to and influence various internal stakeholders, both within and beyond the team.
What Does a Senior Artificial Intelligence/Machine Learning Engineer Do?
Key Responsibilities & Tasks
Develop artificial intelligence (AI)/machine learning (ML) models for production
- Evaluate AI/ML model scaling and packaging codes for refinement
- Assess performance of production-level AI/ML models for scalability
- Manage AI/ML development pipeline and infrastructure
- Lead the extraction, transformation, loading and integration of unstructured data for modelling
- Review scaled AI/ML models to ensure desired performance can be achieved when deployed
- Drive optimisation of AI solutions to increase performance
Deploy AI/ML models
- Oversee the deployment of AI/ML solutions
- Create test plans for post-deployment
- Communicate deployment issues and resolution plans to stakeholders
- Lead the development and deployment of supervised and/or unsupervised techniques for problem solving
Manage AI/ML projects
- Plan the end-to-end deployment of AI/ML solutions from initial testing, deployment, to optimisation in terms of system performance and run time
- Lead project estimations and code reviews
- Set project timelines and work quality guidelines
- Apply project management tools and processes to ensure project is cost-effective
- Communicate project objectives at critical junctions to obtain buy-in from all stakeholders
- Deliver scalable AI/ML solutions
Do You Have the Skills for This Role?
A Senior Artificial Intelligence/Machine Learning Engineer needs 3 core competencies. Here's what's required and at what level.
Must-Have Skills (Advanced)
Developing People
AdvancedInteracting with Others
Transdisciplinary Thinking
AdvancedThinking Critically
Supporting Skills
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 Senior Artificial Intelligence/Machine Learning Engineer requires 22 technical skills at specific proficiency levels.
Business Needs Analysis
Level 5Business and Project Management
Lead comprehensive analysis to understand underlying drivers and present a compelling business case for proposed IT solutions
Computer Vision Technology
Level 5Development and Implementation
Build spatial sensing and spatial reasoning systems
Data Design
Level 5Design and Architecture
Establish a strategy for the creation of large-scale data models and structures and spearhead the implementation of database technology, architectures, software and facilities
Data Governance
Level 5Governance and Compliance
Develop organisation practices and standards for handling data throughout their lifecycle, resolve breaches, and oversee transfer of data between organisations
Data Strategy
Level 5Strategy Planning and Implementation
Establish data management strategies to extract maximum value from information assets and support decision-making and business processes
Emerging Technology Synthesis
Level 5Business and Project Management
Establish internal structures and processes to guide the exploration, integration and evaluation of new technologies
Intelligent Reasoning
Level 5Development and Implementation
Evaluate, design and build intelligent software systems
Pattern Recognition Systems
Level 5Development and Implementation
Develop intelligent systems using machine learning techniques
Problem Management
Level 5Operations and User Support
Establish problem management strategies, protocols, and mechanisms to guide the prevention, resolution and minimisation of problems and their effects
Project Management
Level 5Business and Project Management
Lead end-to-end management of large programmes or multiple projects concurrently, coordinating project interdependencies
Self-Learning Systems
Level 5Development and Implementation
Design and develop self-learning systems using reinforcement learning and evolutionary learning techniques
Stakeholder Management
Level 5Stakeholder and Contract Management
Define a strategic stakeholder management roadmap, and lead critical discussions and negotiations, addressing escalated issues or problems encountered
Text Analytics and Processing
Level 5Development and Implementation
Implement advanced machine learning techniques in building natural language processing (NLP) models for performing common text processing tasks
Change Management
Level 4Business and Project Management
Recommend business activities required to integrate and roll out new changes and drive the execution of change control procedures, engaging stakeholders in the process
Cloud Computing
Level 4Development and Implementation
Develop plans to implement cloud solutions
Computational Modelling
Level 4Development and Implementation
Develop and utilise new algorithms and advanced statistical models to enable the production of desired outcomes
Configuration Tracking
Level 4Development and Implementation
Develop policies, processes and guidelines for the organisation's configuration management and tracking
Data Engineering
Level 4Development and Implementation
Translate business requirements into data structures and processes to standardise data, verify data reliability and validity, store, extract, transform, load and integrate data
Database Administration
Level 4Operations and User Support
Plan for installation, configuration and upgrading of databases and oversee database maintenance, troubleshooting, back up and recovery activities
Security Architecture
Level 4Design and Architecture
Design a security blueprint and direct the design of a robust and coherent security architecture, based on a suite of security solutions and key design principles
System Integration
Level 4Development and Implementation
Determine interoperability of system components and develop a system integration plan
Test Planning
Level 4Development and Implementation
Define testing objectives, and design a master test plan including a series of systematic test procedures to achieve them
European Skills Framework
ESCOSkills and knowledge areas required for this occupation based on European classification.
Essential
Optional
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