The posting
About the Role Impact you will make Join a group of highly talented and motivated data scientists, data engineers, and AI engineers with significant healthcare experience. Define and evolve the enterprise architecture for FinThrive's AI ecosystem, ensuring AI capabilities are effectively integrated across products, platforms, data assets, workflows, and business processes. Partner with business, product, engineering, security, and technology leaders to establish a scalable and sustainable AI strategy that accelerates innovation while ensuring security, compliance, reliability, and operational excellence across the organization. Build the architectural foundation that enables FinThrive to leverage Agentic AI, intelligent automation, machine learning, and advanced analytics to transform healthcare revenue cycle management workflows. What you will do
Define and maintain the architectural vision, principles, standards, and reference architectures for FinThrive's AI platform and AI-enabled products. Lead the long-term evolution of AI capabilities, including agentic AI, retrieval-augmented generation (RAG), knowledge systems, intelligent automation, machine learning, and emerging AI technologies. Define how AI capabilities, intelligent agents, automation services, knowledge systems, and machine learning solutions integrate with existing and future FinThrive products and platforms. Establish enterprise integration patterns, AI architecture standards, governance frameworks, and operational processes that ensure consistency across AI and non-AI systems. Guide strategic technology decisions involving AI platforms, cloud providers, model hosting strategies, vector databases, orchestration frameworks, AI infrastructure, build-versus-buy evaluations, and platform investments. Collaborate with product, engineering, architecture, security, compliance, and executive leadership teams to align AI investments with business strategy, application portfolio roadmaps, and enterprise architecture objectives. Lead architecture reviews for major AI initiatives and provide guidance on system design, scalability, resiliency, interoperability, security, compliance, and cost optimization. Develop multi-year technology roadmaps that identify opportunities for AI enablement across FinThrive's product portfolio and business operations. Create reusable architecture patterns and technical guidance that enable engineering teams to deliver consistent, scalable, secure, and maintainable AI solutions. Research emerging technologies and industry trends, evaluating their applicability to FinThrive's healthcare revenue cycle management and AI strategies. Mentor Lead AI Engineers, Senior II AI Engineers, and other technical leaders while fostering a culture of architectural excellence, collaboration, innovation, and continuous learning. Provide hands-on technical leadership through prototypes, proof-of-concepts, design reviews, and critical problem resolution when necessary.
What you will bring
Bachelor's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Engineering, Software Engineering, or a related technical discipline. 15+ years of experience in software engineering, platform engineering, machine learning, data engineering, solution architecture, enterprise architecture, or related technical leadership roles. 8+ years of experience designing enterprise AI, machine learning, analytics, or intelligent automation solutions. Deep understanding of generative AI, agentic AI, retrieval-augmented generation (RAG), natural language processing, machine learning, information retrieval, intelligent automation platforms, and techniques for model adaptation including prompt engineering, fine-tuning, parameter-efficient fine-tuning (PEFT), and retrieval-based approaches. Experience evaluating and selecting model customization strategies, including prompting, RAG, fine-tuning, continued pretraining, and domain-specific model adaptation. Experience designing enterprise-scale architectures across cloud, application, integration, data, security, and AI domains. Experience architecting AI solutions utilizing platforms such as Azure AI, Azure OpenAI, Azure AI Search, Databricks, AWS Bedrock, SageMaker, or equivalent technologies. Strong understanding of modern AI frameworks, orchestration platforms, open-weight models, evaluation frameworks, observability tooling, and AI governance practices. Experience establishing architecture standards, technology roadmaps, governance processes, and enterprise engineering practices. Demonstrated experience designing architectures that span multiple applications, platforms, business domains, and technology stacks. Proven ability to influence technical direction across multiple teams, products, and organizational boundaries. Excellent communication, presentation, and stakeholder-management skills with the ability to communicate architecture strategy and technical tradeoffs to executive leadership. Strong understanding of software development lifecycle practices, DevSecOps, MLOps, platform engineering, security principles, and cloud-native architectures.
What we would like to see
Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Software Engineering, Enterprise Architecture, or a related technical discipline. Experience serving as an Enterprise Architect, AI Architect, Principal Architect, Solution Architect, or similar strategic architecture role. Experience building and governing AI solutions in regulated industries such as Healthcare, Financial Services, Insurance, or Consumer Credit. Experience developing AI solutions utilizing healthcare standards, claims data, clinical data, FHIR, HL7, medical coding systems, knowledge graphs, or other healthcare-specific technologies. Experience defining responsible AI frameworks, AI governance programs, model risk management practices, and enterprise AI policies. Experience evaluating and deploying open-weight foundation models and enterprise AI platforms in both cloud-hosted and self-hosted environments. Experience fine-tuning, evaluating, deploying, or governing open-weight foundation models using techniques such as supervised fine-tuning (SFT), parameter-efficient fine-tuning (LoRA/PEFT), reinforcement learning methods, or synthetic data generation. Experience developing enterprise technology roadmaps and guiding platform modernization initiatives. Experience leading enterprise-scale multi-agent platforms, intelligent automation programs, or large-scale AI transformation initiatives. Contributions to open-source projects, patents, publications, conference presentations, industry forums, or other thought leadership activities.
