Lead AI Engineer

TAG - The Aspen Group•Chicago, IL

21h•$170,000 - $200,000•Onsite

About The Position

As part of our continued investment in AI-driven innovation, we are looking for a Lead 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 & frameworks, LLM-based architecture, and software engineering.

Requirements

Master’s degree in AI/ML, Computer Science, or related technical field.
2+ years of experience in Generative AI and Agentic AI engineering.
8+ years of experience in AI/ML engineering, software engineering, or platform architecture.
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 with one or more of these 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.

Nice To Haves

Experience with AI governance frameworks, responsible AI practices, and observability (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 & frameworks.

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.