Principal Artificial Intelligence Engineer

About the position

Key responsibilities:

• Provide leadership in the provision of technical solutions to client programmes;
• Propose and/or contribute to the strategic investment plan and implement Parliamentary Grant, Contract Research and Development and other grant-funded projects;
• Research, develop and test innovative solutions in AI and remain well-versed in new approaches in tools, methods or technologies in the evolving AI space;
• Advance theoretical AI approaches to deployable solutions, i.e. implemented and deployed into large-scale production systems
• Develop technology demonstrators to contribute to an increase in new localised technologies
• Supervise/mentor junior staff in the area(s) of expertise;
• Generate and lead science engineering and technology (SET) activity outputs e.g. reports, guidelines, system requirements, peer-reviewed publications, and technology and software packages;
• Understand and interpret client requirements by contributing to user requirement analysis and/or well-articulated proposals;
• Participate in external task teams or committees in relevant domains;
• Provide technical support on business development and participate in client interactions;
• Manage projects through planning of appropriate processes and policies to ensure Intellectual Property (IP) protection and technology transfer;

Desired Skills:

• o Computer vision tasks
• o Building multimodal models
• o Data platform engineering
• o Natural language processing and understanding
• o Cloud-based platforms
• AWS
• IBM Cloud
• Azure

Desired Work Experience:

• 5 to 10 years

Desired Qualification Level:

• Degree

About The Employer:

Qualifications, skills and experience




- An Honour's degree in computer science/engineering, electrical/electronic engineering, information technology or related fields with at least seven years' experience in artificial intelligence (AI) /machine learning (ML) and software development in artificial intelligence technologies;
- A Master's degree will be advantageous;
- Experience in the following is required:
o Applied machine-learning with regression, classification, etc. models for supervised learning;
o Computer vision tasks
o Natural language processing and understanding,
o Building multimodal models;
o Unstructured data;
o Big-data unsupervised learning;
o Data platform engineering;
o Demonstrated experience in bringing theoretical machine-learning approaches illustrated in academia research papers to actual implementations, i.e. implemented and deployed into large-scale production system certain advanced ML and applied ML algorithms;
o Cloud-based platforms: AWS, IBM Cloud, Azure;
o Proficiency and experience building AI models with a deep learning framework such as TensorFlow, Keras or Theano;
o Ability to select hardware (and relevant production/deployment knowledge) to run an ML model with the required latency;
- Business Development and financial acumen;
o Ability to construct scopes and proposals for potential clients.
- Excellent communication and presentation skills;
- Exposure to Project Management;
o Technical lead for complex projects involving multiple stakeholders.
- Understanding of transforming/implementing software/algorithms for use in real-life systems would be advantageous;
- Conflict management skills will be advantageous;
- Knowledge of programming in modern object orientated languages, with Open-Source development tools and platforms;
- Ability to apply resource management techniques to run an ML model with the required latency;
- Ability to demonstrate Object-orientated software engineering skills;
- Strong quantitative skills (mathematics/statistics/computer science);
- Demonstrated ability to supervise/mentor/develop junior staff;
- Demonstrated skills in: analytical thinking, flexibility and adaptability, investigative orientation, planning and organising, problem solving, verbal and written communication, team work, self-management (planning, prioritising and time management - includes the ability to work independently), systems level thinking, multi-disciplinary knowledge.