AI and DATA Engineer

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.