Summary

TheAIArchitectis a member of a highlymotivated CAATechteam responsible for accelerating the creation of opportunity through the strategic use of data.In this role, you will design andarchitect intelligent systemsthat transform complexinformationinto structured knowledge and actionable insights using modern AI,LLMs,machine learning, and generativeapproachesto solve complex business problems, while defining architectural patterns, technical standards, and integration approaches that enable scalable enterprise adoptionand implementation.You willoperateacross the full lifecycle,from experimentation and evaluation to deployment andoptimization,guiding system design anddevelopingsolutionsthat ingest, interpret, and reason over large volumes ofdata. The ideal candidate combines strong software engineering fundamentals with handson experience buildingand architectingapplied AI systems thatoperatereliably in production environments,establishbest practices,and deliver measurable business impact.

Responsibilities

AI Architecture

• Define and evolve reference architectures for AI, machine learning, and generative AI systems

• Establish technical standards, patterns, and best practices for scalable and secure AI development

• Guide platform decisions across data, model development,and deployment infrastructure

• Evaluate and recommend tools, frameworks, and vendors aligned with longterm architectural direction

Development ofAISolutions

• Design and implement endtoend AI workflows forcomplex data interpretation, knowledge extraction, and reasoning

• Build systems that convert unstructured content into structured representations

• Develop andoptimizeretrieval approaches that combine semantic and lexical search techniques

• Prototype, evaluateapproaches,test feasibility,and productionize generative AI solutions for realworld use cases

• Optimizesystems for latency, cost, and throughput

Evaluation, Reliability, and Quality

• Design and implement evaluation approaches to assess performance, relevance, and effectiveness of AI solutions

• Apply techniques to improve consistency, accuracy, and trustworthiness of outputs

• Implement methods for scoring, explainability, and ongoing quality assurance

• Monitor deployedsolutionsand data pipelines for performance, stability, and data quality over time

• Incorporate responsible AI practices including transparency, bias mitigation, and risk controls

Collaboration and Communication

• Work closely with crossfunctional team to align data science initiatives with business priorities

• Partner with leadership toidentifyand prioritize highimpact opportunities for data science applications

• Present insights and recommendations through clear visualizations tailored for audiences acrossdifferent rolesandexpertiselevels

Required Capabilities

• Minimum10+ years buildingAI, MachineLearningandNLP solutions