[Job - 26904] Data & AI Architect, Brazil

We are tech transformation specialists, uniting human expertise with AI to create scalable tech solutions. With over 8,000 CI&Ters around the world, we’ve built partnerships with more than 1,000 clients during our 30 years of history. Artificial Intelligence is our reality. We are looking for a Senior Data Architect with AI Expertise to join our team. In this key role, you will be the leader who turns ideas into great data and AI-driven systems using the latest technology. Your goal is to improve the reliability of our data infrastructure while enabling intelligent, ethical, and auditable AI capabilities across the platform. You will guide newer team members, teaching them to grow in their roles. You will work with different teams to ensure our data and AI architecture works well for everyone, explaining complicated concepts in simple terms. It's important that you are skilled in using modern data technology tools, AI/ML frameworks, and programming languages, and that you can guide the team through updates and changes. In short, you're the key person who will elevate our data and AI capabilities and keep our team learning and thinking in new ways. This position requires the candidate to be located in Austin, Texas, for 100% in-person work at the client site. Responsibilities Define and communicate the long-term data architecture vision, aligned with the business strategy and AI innovation roadmap. Lead the evaluation and selection of technologies, frameworks, and standards for implementing the data architecture, including AI/ML infrastructure. Establish and promote best practices for data management, governance, and quality. Actively participate in defining and executing the organization's data and AI strategy. AI & Intelligence Layer Design and architect the Intelligence Layer for AI-driven assessment and scenario generation capabilities. Ensure AI solutions are auditable, transparent, and incorporate bias mitigation strategies. Define data pipelines and infrastructure to support ML model training, deployment, and monitoring. Collaborate with data scientists and ML engineers to optimize data flows for AI workloads. Technical Leadership Collaborate with leadership teams and stakeholders to ensure the data architecture meets business needs and supports clinical workflows. Identify and anticipate complex technical challenges, proposing innovative and effective solutions. Provide advanced technical guidance and support to the data development, architecture, and AI teams. Ensure compliance with data security regulations and healthcare industry standards (HIPAA, GDPR, etc.). Integration & Hybrid Infrastructure Design data architecture that supports hybrid environments with offline-first capabilities. Define integration patterns for the API gateway connecting LMS providers and healthcare hardware. Ensure data synchronization strategies for low-connectivity clinical environments. Culture & Mentorship Promote a culture of innovation and technical excellence in the data and AI areas. Mentor and empower other team members by sharing knowledge and experience. Represent the data architecture in internal and external technical and strategic forums. Develop and maintain strategic relationships with suppliers and technology partners. Must Have Cloud Architecture: Experience with cloud platforms. Data Warehousing: Experience with Snowflake or similar modern data warehousing solutions. Data Engineering: Experience with DBT, data modeling, ELT, and performance tuning. AI/ML Infrastructure: Understanding of data architecture requirements for AI/ML workloads, including feature stores, model registries, and ML pipelines. Healthcare Context: Ability to design data systems that comply with healthcare regulations and support clinical workflows. Nice to Have Snowflake Certification or equivalent cloud data platform certifications. Experience with data governance, security, and privacy frameworks. Experience with AI/ML platforms (Azure ML, Databricks, SageMaker, or similar). Knowledge of MLOps practices and tools. Experience with vector databases and LLM integration patterns. Understanding of bias detection and AI ethics principles. Experience with real-time data streaming and event-driven architectures. Knowledge of accessibility standards (WCAG 2.1 AA) and their data implications. Experience in healthcare or clinical systems.