1703, Al Mansoor Tower, Salam Street, PO Box: 52300, Abu Dhabi, UAE.
Roles & Responsibilities:
1. Project Leadership and Execution: Lead NLP and General AI projects from conception to deployment, ensuring projects align with the strategic business objectives. Act as the project manager and primary point of contact for internal stakeholders and cross-functional teams.
2. Advanced Model Development: Design, develop, and refine state-of-the-art NLP models and General AI systems. Focus on enhancing text understanding, generation, and translation capabilities within the business framework. Implement models like transformers and GANs tailored to specific business needs.
3. Innovation and Research: Drive innovation by exploring emerging trends and technologies in AI. Conduct research to push the boundaries of what is possible with NLP and General AI, translating novel ideas into practical business solutions.
4. Data Strategy and Architecture: Develop and oversee the data strategy for training sophisticated AI models. Ensure the architecture supports scalable AI solutions, focusing on data integrity, preprocessing, and efficient data storage and retrieval systems.
5. Collaboration and Integration: Collaborate with other AI teams and business units to ensure that NLP and General AI solutions are seamlessly integrated with existing systems and workflows. Foster strong relationships with IT, product development, and operational teams to ensure technology alignment.
6. Stakeholder Engagement and Communication: Regularly engage with business stakeholders to identify opportunities for AI to add value. Translate complex AI concepts and project results into actionable insights for both technical and non-technical audiences.
7. Mentorship and Team Development: Mentor junior data scientists and engineers, promoting skill advancement and professional growth within the team. Lead workshops and training sessions to disseminate AI knowledge and best practices across the organization.
8. Quality Assurance and Model Governance: Establish rigorous testing and validation processes to ensure the accuracy and reliability of AI models. Develop guidelines for ethical AI use and model governance, maintaining compliance with industry standards and regulations.
9. Technical Proficiency and Tool Development: Maintain proficiency with the latest AI frameworks and tools (e.g., TensorFlow, PyTorch, Hugging Face). Develop custom tools and libraries that enhance team productivity and model performance.
Specific Accountability:
1. AI Solution Architecture: Lead the architectural design and integration of complex AI systems, employing collaborative development with engineers and domain experts to innovate solutions at the intersection of AI and business processes.
2. Advance Predictive Analytics: Utilize state-of-the-art forecasting algorithms powered by Large Language Models to construct predictive models that accurately forecast market trends and consumer behavior, enhancing strategic decision-making in financial contexts.
3. Cutting-Edge Algorithm Development: Spearhead the development and implementation of advanced algorithms incorporating techniques such as Retrieval-Augmented Generation (RAG) and Low-Rank Adaptation (LoRA), pushing the boundaries of what is achievable with current AI technologies.
4. NLP-Driven Interactive Technologies: Engineer and optimize NLP-driven tools, such as chatbots and virtual assistants, utilizing neural networks and transformer models to ensure nuanced understanding and generation of natural language, thereby elevating the user interaction experience.
5. Continuous Technological Upgrade: Maintain a rigorous program of continuous learning and application of the latest advancements in AI, especially in areas like model compression, federated learning, and real-time analytics, to drive ongoing enhancements in financial services.
6. Scalable AI Deployment: Oversee the deployment of AI models across distributed cloud environments, focusing on scalability and fault tolerance, leveraging platforms such as AWS SageMaker for model training and deployment, and using technologies like Docker and Kubernetes for orchestration.
7. Advanced Data Visualization and Communication: Develop advanced visualization tools and techniques to interpret and communicate complex model outputs to stakeholders, utilizing libraries such as Matplotlib, Seaborn, or interactive tools like Plotly and D3.js for dynamic data representation.
8. Cloud Infrastructure and AI Model Optimization: Expertly manage and fine-tune AI model performance across various cloud infrastructures, including AWS, Azure, and G42, applying best practices in cloud security and cost optimization.
9. Quantitative Analysis and Statistical Modeling: Apply rigorous statistical analysis and exploratory data analysis techniques to large and complex datasets, using advanced statistical models like generalized linear models (GLM), decision trees, and clustering algorithms to uncover insights.
10. Deep Learning and NLP Algorithms: Develop and refine deep learning models, particularly focusing on NLP applications, using libraries such as TensorFlow, PyTorch, and spaCy, ensuring the models are well-suited for tasks like sentiment analysis, topic modeling, and language translation.
11. Strategic Problem-Solving and Innovation: Identify and resolve model and data discrepancies, prototyping and testing alternatives that maximize business impact rather than merely adopting new trends.
12. Professional Development and Technological Leadership: Actively contribute to the professional growth of team members and the AI community, sharing knowledge through workshops, seminars, and publications, fostering an environment of innovation and continuous improvement
Minimum Qualification & Experience
1. Master’s degree or higher in Data Science, Computer Science, Statistics, or a closely related field. A doctoral degree (PhD) is highly desirable, particularly if it focuses on AI, machine learning, or computational statistics, which will demonstrate deep theoretical knowledge and research capabilities.
2. Working experience in field of Generative AI, Machine learning or NLP, ideally within the academic research or financial sector, encompassing the development and operational deployment of machine learning algorithms.
3. A proven track record of impactful research with publications in respected journals and significant contributions to major projects, particularly those involving advanced AI technologies
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