Staff AI Engineer
The Aspen Group (TAG) is one of the largest and most trusted retail healthcare business support organizations in the U.S., supporting over 20,000 healthcare professionals and team members with nearly 1,500 health and wellness offices across 48 states. TAG operates in four distinct categories: dental care, urgent care, medical aesthetics, and animal health. Partnering with independent practice owners and clinicians, TAG provides centralized business support services to five consumer-facing businesses: Aspen Dental, ClearChoice Dental Implant Centers, WellNow Urgent Care, Chapter Aesthetic Studio, and Lovet.
About the role
As part of our continued investment in AI-driven innovation, we are looking for a Staff AI Engineer to join our growing AI team. This is a hands-on role delivering innovative solutions for the healthcare enterprise. The ideal candidate will bring deep expertise in modern AI systems, multi-agent systems and frameworks, LLM-based architecture, and software engineering.
Responsibilities
- Architect and develop enterprise-scale multi-agent systems leveraging LLMs and autonomous agent frameworks using Google ADK, Agentspace, MCP, RAG, and A2A orchestration.
- Design and implement RAG pipelines using BigQuery and Vertex AI Engine for knowledge grounding and factually accurate responses.
- Optimize agents for orchestration, knowledge grounding, multi-step reasoning, and decision-making.
- Design and implement distributed training workflows, online inference systems, and low latency serving architectures optimized for real-world performance, using Google cloud-native services.
- Engineer scalable, secure, compliant, and production-grade AI fabric and AI agent workflows using Vertex AI and modern cloud-native technologies.
- Create reusable agent orchestration layers, observability hooks, and governance frameworks that accelerate Agentic AI adoption across TAG brands.
- Partner with cross-functional stakeholders in translating business requirements into technical specifications.
- Own the full AI development lifecycle from data collection and implementation to deployment and monitoring.
- Implement intelligent observability and automation strategies to ensure AI system reliability and performance at scale.
Requirements
- BS in Computer Science or related technology field, or equivalent experience.
- 2+ years of experience in Agentic AI engineering.
- 4+ years of experience in AI/ML engineering.
- 8+ years of experience in software engineering or platform engineering.
- Proven track record of building and deploying production-grade AI/ML systems at scale.
- Deep understanding of modern AI model architectures (e.g., transformers, diffusion models) and system design.
- Strong hands-on expertise with Vertex AI (including model training, pipelines, orchestration, deployment, and monitoring) and Google’s Agentic AI stack.
- Hands-on experience with one or more agent orchestration frameworks: Google ADK/Agentspace, LangChain, LangGraph, LlamaIndex, CrewAI, or AutoGen.
- Proficiency in Python, LLM integration workflows, MCP (Model Context Protocol) for tool integration, and A2A (Agent-to-Agent) orchestration for multi-agent workflows.
- Expertise in distributed training, online inference, and low latency serving architectures.
- Experience with Kubernetes, Cloud Run, and Dataflow/PubSub for scalable deployment.
Preferred Qualifications
- Experience with AI governance frameworks and responsible AI practices (Vertex AI Model Monitoring, BigQuery logging, Looker dashboards).
- Contributions to open-source AI projects or publications in leading AI/ML conferences.
- Experience with multi-modal models and advanced optimization strategies and frameworks.
- Experience automating, architecting, and governing production-grade MLOps infrastructure to scale, optimize, and observe AI workloads.
Pay
Annual Salary Range: $170,000–$200,000/year
Benefits
- Paid time off
- Health, dental, and vision insurance
- 401(k) savings plan with company match