Lead AI Architect
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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
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