Location: Remote, USA
Employment Type: Full-Time | Exempt
About the Role
FinThrive is building the future of healthcare revenue cycle management through advanced AI capabilities. As Lead AI Architect, you will define and evolve the enterprise architecture for FinThrive's AI ecosystem—enabling Large Language Models (LLMs), Agentic AI, and advanced automation to transform how we deliver value.
This is a strategic, high-impact role. You’ll partner with business, product, architecture, and engineering leaders to set a scalable AI vision, shape technology decisions, and guide the organization in responsible, secure, and innovative AI adoption.
What You Will Do
- Define and maintain AI architecture standards, reference models, and technology roadmaps.
- Architect and integrate Agentic AI systems, Generative AI, Document Intelligence, and retrieval-augmented generation (RAG) into FinThrive products and platforms.
- Lead fine-tuning and customization of AI models using advanced techniques:
- Supervised Fine-Tuning (SFT)
- Parameter-Efficient Fine-Tuning (PEFT)
- Low-Rank Adaptation (LoRA)
- Continued Pretraining for domain-specific adaptation
- Establish best practices for prompt engineering, model evaluation, observability, and AI governance.
- Provide architectural leadership for MLOps, Responsible AI, and compliance within a regulated healthcare environment.
- Mentor technical leaders in AI design patterns, orchestration frameworks (e.g., LangChain, Semantic Kernel), and vector database solutions.
- Collaborate on build vs. buy decisions, cloud platform optimization, and scalable deployments using Azure AI, Azure OpenAI, AWS Bedrock, or equivalent platforms.
What You Bring
- 15+ years in software engineering, enterprise architecture, or AI platform leadership roles.
- 8+ years designing AI-driven solutions for enterprise-scale applications.
- Proven hands-on expertise in model fine-tuning (SFT, LoRA, PEFT) and domain-specific LLM adaptation.
- Strong command of:
- Generative AI, Agentic AI, RAG, Document Intelligence
- AI/ML frameworks and orchestration tools (LangChain, Semantic Kernel)
- Cloud AI platforms: Azure AI/OpenAI, AWS Bedrock, Databricks
- Knowledge of MLOps, AI observability, and governance practices.
- Excellent stakeholder engagement and communication skills for influencing enterprise technology strategy.
Preferred Qualifications
- Master’s degree in Computer Science, AI, Data Science, or related field.
- Experience in regulated industries (Healthcare, Financial Services, Insurance).
- Contributions to open-source AI projects, patents, publications, or thought leadership in the AI space.
Skills
Enterprise Architecture | AI Architecture | Generative AI | Agentic AI | Retrieval-Augmented Generation (RAG) | Document Intelligence | Model Fine-Tuning (SFT, LoRA, PEFT) | Azure AI | AWS Bedrock | Vector Databases | Prompt Engineering | MLOps | Responsible AI | AI Governance