Senior Artificial Intelligence (AI) Specialist

Tawteen

AI Solution Development:

Design and Architecture: Lead the design and development of AI systems that address key business challenges, ensuring these systems are robust, scalable, and integrate seamlessly into the company s technology landscape.

Algorithm Selection: Research and select appropriate AI models, machine learning algorithms, and deep learning frameworks for specific tasks (e.g., regression, classification, clustering, recommendation systems, etc.).

Model Training and Evaluation: Train AI models using supervised, unsupervised, and reinforcement learning techniques; evaluate model performance using metrics like accuracy, precision, recall, and ensure they meet performance standards.

NLP Implementation: Develop and optimize natural language processing algorithms to enhance language understanding in various applications, such as chatbots, sentiment analysis, and automated customer service.

Data Strategy and Management:

Data Preparation: Collaborate with data engineering teams to design and implement ETL (Extract, Transform, Load) pipelines, ensuring data is clean, organized, and usable for AI applications.

Data Governance: Ensure the ethical use of AI through appropriate data governance practices, including adherence to data privacy standards, such as GDPR and CBO s Financial Consumer Protection Regulatory Framework, and the implementation of responsible AI principles.

Data Augmentation: Enhance data through augmentation techniques, using synthetic data where necessary to bolster training datasets.

System Integration and Deployment:

Integration: Work with software developers and IT teams to deploy AI solutions and integrate them with existing systems, such as ERP, CRM, and cloud platforms.

Continuous Improvement: Develop automated systems to monitor AI performance post-deployment and ensure continuous learning, allowing the system to adapt and improve over time.

Version Control and Reproducibility: Utilize version control tools to ensure all AI models and code are reproducible and traceable.

Automation and Process Optimization:

AI-Driven Automation: Develop AI-based automation solutions, such as intelligent workflows, to streamline business processes e.g., supply chain management, customer service, and IT operations.

Predictive Analytics: Implement predictive models to forecast business metrics like customer churn, sales trends, and operational efficiency.

Collaboration and Consultation:

Cross-Functional Collaboration: Work closely with cross-departmental teams (Marketing, Operations, Finance, etc.) to identify AI opportunities and translate business needs into technical requirements.

Training and Knowledge Sharing: Educate stakeholders and internal teams on AI trends, tools, and best practices, ensuring widespread adoption and understanding of AI across the organization.

Research and Development:

Innovation: Stay abreast of the latest advancements in AI research, including emerging frameworks, algorithms, and hardware optimizations (e.g., GPUs, TPUs, quantum computing).

Experimentation: Lead experiments on cutting-edge AI applications, including generative AI, reinforcement learning, and neural network innovations.

R&D Roadmaps: Contribute to AI R&D roadmaps, proposing initiatives that can push the boundaries of what the company can achieve with AI in areas like personalization, AI ethics, and human-machine collaboration.

Technical Skills:

Programming Languages: Proficiency in Python, R, Java, or C++, with strong hands-on experience in AI/ML libraries such as TensorFlow, PyTorch, Keras, and scikit-learn.

Data Handling: Strong knowledge of SQL and NoSQL databases, data lake architectures, and big data processing technologies

Cloud Proficiency: Experience with AI services on cloud platforms like AWS (SageMaker), Microsoft Azure (Cognitive Services), GCP (Vertex AI), Oracle cloud database.

Modeling Techniques: Expertise in deep learning, neural networks (CNNs, RNNs, LSTMs, GANs), NLP, computer vision, and reinforcement learning.

Software Engineering: Knowledge of software development methodologies, including Agile, and version control systems

Power BI & Power Query; Data Visualization, DAX

Both Front End & Back end of data migration integration cleaning

Preferable knowledge in Oracle