AI Architect - Enterprise LLM & Agent Platform Lead

Ethereum Technologies; Dallas (TX); On-site; USD 180,000 - 240,000; Full time

Job summary

Ethereum Technologies seeks an experienced AI Architect to lead business and technical strategy for enterprise AI solutions. You will translate complex problems into agent-driven blueprints and guide stakeholder discussions across risk, security, and QE teams.

You will design RAG-based agents, integrate enterprise data sources, and ensure scalable, observable, and cost-efficient AI platforms while establishing guardrails for safety and compliance.

Qualifications

• Deep expertise in LLMs, prompt engineering, and reasoning frameworks.
• Experience with agentic frameworks and enterprise data integration.
• Knowledge of RAG, embeddings, and vector databases.
• Experience with model risk management and governance is implied.

Responsibilities

• Translate business problems into agent-based solution blueprints.
• Partner with senior stakeholders to identify high-impact use cases.
• Provide executive-level guidance on agentic AI adoption and roadmaps.
• Support client conversations, RFPs, solution pitches, and thought leadership.
• Architect RAG-based and tool-augmented agents using enterprise data sources, APIs, and workflows.
• Ensure scalability, resilience, observability, and cost optimization of agent platforms.
• Establish AI guardrails covering safety, bias, explainability, auditability, and regulatory compliance.
• Partner with Risk, Legal, Security, and QE teams to ensure model risk management and enterprise readiness.
• Drive standards for agent testing, validation, and certification.

Skills

LLMs & prompt engineering

Agent frameworks

Python programming

Cloud platforms

Tools

LangGraph

AutoGen

CrewAI

Semantic Kernel

LoRA/QLoRA

Job description

Ethereum Technologies seeks an experienced AI Architect to lead business and technical strategy for enterprise AI solutions. You will translate complex problems into agent-driven blueprints and guide stakeholder discussions across risk, security, and QE teams.

You will design RAG-based agents, integrate enterprise data sources, and ensure scalable, observable, and cost-efficient AI platforms while establishing guardrails for safety and compliance.