Senior AI Specialist

Minimum requirements:

• Advanced Diplomas/National 1st Degrees
• B.Sc Computer Science, B.Com Informatics, Engineering Degrees (preferred) Candidates should have development experience in generative models such as M365 Co-Pilot, Bing Chat, GitHub Co-Pilot, GPT, and Transformer models. Candidates should identify the use case, define POC generative AI models, and test the models extensively to ensure they meet quality/performance.
• Proven experience in developing and deploying Gen AI solutions in a real-world setting.
• Programming skills in languages such as Python, C++ or Java will be advantageous.
• Understanding of machine learning, deep learning, and natural language processing techniques.
• Prompt Engineering skills would be an advantage.
• Furthermore, one should have a good understanding of the ethical, social, and legal implications of developing and deploying Gen AI systems.
• Excellent problem-solving skills and the ability to work on complex, unstructured problems.
• Strong communication and presentation skills, with the ability to convey complex AI concepts to non-technical stakeholders.
• Experience with cloud computing platforms (e.g., AWS, Azure, Google Cloud) is a plus.
• Experience in leading and mentoring junior team members is a plus.

Technical / Professional Knowledge

• Technical skills
• Product management
• Project Management
• Financial management
• Strategy planning and execution
• Vendor Management Skills
• Stakeholder management
• Governance, Risk and Controls
• Relevant regulatory knowledge

Responsibilities:

• Gen AI Strategy: Collaborate with the leadership team to define and execute the company's AI strategy, identifying opportunities to leverage Gen AI to drive business growth and innovation.
• Agile Leadership: Lead Gen AI squads from conception to implementation, ensuring that they are delivered on time and meet the highest quality standards.
• Algorithm Development: Develop and optimize machine learning and deep learning algorithms to solve complex business problems.
• Model Training and Evaluation: Build, train, and evaluate Gen AI models using large datasets, continuously improving model performance and accuracy.
• Mentorship: Provide guidance and mentorship to junior Gen AI team members, fostering their professional growth and development.
• Research and Innovation: Stay current with the latest Gen AI research and technologies, identifying opportunities to incorporate new advancements into our Gen AI solutions.
• Cross-functional Collaboration: Work closely with product managers, engineers, and other stakeholders to integrate AI capabilities into our products and services.
• Ethical AI: Ensure that Gen AI models and solutions adhere to ethical guidelines and data privacy regulations.