Senior Artificial Intelligence Specialist

Job Description Roles & Responsibilities Key Responsibilities: 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 Desired Candidate Profile Qualifications: Educational Requirements: Minimum: Bachelor s degree in Computer Science, Artificial Intelligence, Data Science, or a related field. Preferred: Master s degree in a relevant discipline, with specialization in AI, machine learning, or data science. Experience Requirements: AI and ML Expertise: 7+ years of hands-on experience in AI and machine learning, with a proven track record of building and deploying large-scale AI systems. Industry Exposure: Experience in AI implementation within a financial services, retail, healthcare, or technology company is highly desirable. Project Leadership: Demonstrated experience leading AI-focused projects from conception to production, including experience managing teams or mentoring junior staff. Company Industry InternetE-commerceDotcom Department / Functional Area IT Software Keywords Senior Artificial Intelligence Specialist Get real-time job updates only on our App