Key Responsibilities Design and develop AI-driven solutions that address strategic business needs and enhance enterprise capabilities. Transform existing business processes to be AI-first or AI-enabled, ensuring seamless integration with current systems. Collaborate with cross-functional teams to identify AI opportunities and create roadmaps for AI implementation. Design within industry standards User Experience, Security, Financial discipline and Responsible AI guidelines and frameworks. Develop and maintain AI models, ensuring their relevance and effectiveness in addressing business challenges. Implement model management and AI governance practices to ensure compliance, transparency, and ethical AI usage. Integrate AI solutions into enterprise systems and workflows, optimizing for performance and scalability. Conduct thorough testing and validation of AI models to ensure accuracy, reliability, and robustness. Model Development: Design, develop, and optimize AI and ML models to meet specific project needs. Data Analysis: Perform data collection, cleansing, and preprocessing to prepare robust datasets for modeling. Algorithm Selection: Assess and select appropriate algorithms and tools for model creation. Performance Evaluation: Evaluate model performance using relevant metrics and refine models as needed. Documentation: Maintain detailed documentation of model development processes, parameters, and outcomes. Collaboration: Work closely with cross-functional teams to integrate AI solutions into business operations. Innovation: Stay updated with the latest advancements in AI and ML technologies and implement best practices. Technical Disciplines Data Science: Proficiency in data analysis, data mining, and data visualization techniques to extract valuable insights from large datasets. Modelling: Expertise in developing and deploying machine learning models, including supervised, unsupervised, and reinforcement learning techniques. Process Design: Ability to design and optimize business processes, ensuring they are aligned with AI capabilities and objectives. Model Management: Experience in model lifecycle management, including versioning, monitoring, and retraining of AI models. Model and AI Governance: Knowledge of best practices in AI governance, including ethical considerations, bias mitigation, and regulatory compliance. Required Skills and Competencies Data Science (DATS): Proficiency in data science techniques and methodologies to analyze complex datasets. Machine Learning (MLNG): Expertise in machine learning frameworks, libraries, and tools. Programming (PROG): Strong programming skills in languages such as Python, R, or Java. Analytics (INAN): Ability to derive insights from data through statistical and analytical methods. Algorithm Design (ALDS): Knowledge of algorithm design principles and their application in solving real-world problems. Business Analysis (BUAN): Understanding business requirements and translating them into technical solutions. Testing (TEST): Experience with model testing, validation, and quality assurance processes. Project Management (PRMG): Skills in managing AI/ML projects from conception to deployment. Qualifications Bachelor's or Master's degree in Computer Science, Data Science, Machine Learning, or a related field. 3 years of experience in designing and implementing AI and ML models. Strong problem-solving skills and a proactive approach to identifying solutions. Excellent communication skills to convey complex technical concepts to non-technical stakeholders. Experience with cloud platforms like AWS, Azure, or Google Cloud is a plus