AI Architect

• Review data preparation tasks, and plans to address patterns or anomalies, while ensuring data readiness for advanced modeling and AI. • Review models for complex use cases (e.g., forecasting models, LLM-based solutions), and refine algorithms to meet business needs. • Review plan for smooth deployment into scalable, production-ready solutions. • Review test plans and test results for analytics use cases, while defining optimization standards for model accuracy and stability, in alignment with business goals. • Build models and analytics solutions tailored to business needs. • Ensure quality and scalability across client engagements while actively contributing to knowledge assets and innovation streams. • Leverage tools like SAS and R/Python to create reusable customizations for non-ML, ML, and deep learning algorithms, while enhancing analytics including LLMs, and create innovative, cost-effective solutions. • Review and refine analytics problems; identify data sources and extract from diverse environments. • Oversee analysis execution and drive business insights. • Create monitoring strategies across multiple projects, embedding governance frameworks to ensure robustness, reliability, and risk awareness. • Review monitoring frameworks, refine documentation/reporting templates, and present insights on anomalies or slippages to stakeholders. • Refine documentation strategy across teams, ensuring transparency and reproducibility of complex analytics solutions. • Collaborate with cross-functional teams, ensuring alignment between analytics delivery and business strategy. • Review analytics outputs for adherence to quality frameworks and project commitments. • Recommend improvements to quality metrics and guide team members to align with standards. • Identify and recommend model changes needed for successful deployment. • Engage in creation and refinement of IP assets such as analytics prototypes and accelerators. • Develop insights, whitepapers, and proof-of-concept summaries that highlight innovative thinking. • Review innovative models and applications in non-ML, ML, deep learning, or LLM areas. • Support participation in forums and internal knowledge exchanges. • Deliver training sessions on technical and analytics-specific topics. • Collaborate on content creation and mentor team members through hands- on guidance in live projects. • Provide input for segment and unit-level business plans.