Generative AI Architect

Ethereum Technologies; Plano (TX); On-site; USD 180,000 - 280,000; Full time

Job summary

Ethereum Technologies seeks a senior GenAI Architect to design and implement end-to-end GenAI and agentic AI solutions for enterprise-scale environments.

You will lead architecture across LLMs, RAG, multi-agent workflows, and vector databases on Azure or GCP, guiding projects from POC to production and ensuring security and reliability.

Qualifications

• 8–10+ years in Software Eng, Data Science, ML, or AI.
• 3+ years as Solution/Systems Architect.
• Extensive GenAI deployment in large-scale prod envs.
• Hands-on GenAI, LLMs, RAG, and multi-agent architectures.
• Strong Python experience.
• Experience with LangChain, LlamaIndex, or similar.
• Cloud architecture with Azure or GCP.
• Hands-on with vector DBs (Pinecone/Milvus/Qdrant/PGVector).
• Knowledge of MLOps/LLMOps, deployment and monitoring.
• Experience with SQL, PySpark, distributed computing, APIs.
• Experience with PyTorch or TensorFlow.

Responsibilities

• Architect and implement end-to-end GenAI and agentic AI solutions.
• Design enterprise-grade solutions using LLMs, RAG, and multi-agent workflows.
• Build scalable AI architectures on Azure or GCP.
• Lead GenAI projects from POC to production deployment.
• Integrate GenAI with enterprise apps, APIs, data platforms, and cloud services.
• Establish MLOps/LLMOps practices for deployment and lifecycle management.
• Address security, scalability, governance, reliability, and integration needs.

Skills

GenAI architecture

LLMs & Agentic AI

Python programming

Azure/GCP

LangChain/LlamaIndex

Vector databases

MLOps/LLMOps

PyTorch/TensorFlow

Distributed computing

API design

SQL/PySpark

Tools

Azure

GCP

LangChain

LlamaIndex

CrewAI

AutoGen

Pinecone

Milvus

Qdrant

PGVector

Job description

US Citizen, Green Card, H-1B, OPT-EAD, CPT, Other

Job Description

• Architect and implement end-to-end Generative AI and Agentic AI solutions.
• Design enterprise-grade solutions using LLMs, RAG, multi-agent workflows, and vector databases.
• Build scalable AI architectures on Microsoft Azure or Google Cloud Platform (GCP).
• Lead GenAI solutions from POC/prototype through production deployment.
• Integrate GenAI solutions with enterprise applications, APIs, data platforms, and cloud services.
• Establish MLOps/LLMOps practices for deployment, monitoring, and lifecycle management
• Design solutions considering enterprise security, scalability, governance, reliability, and integration requirements.

Must-Have Skills

• 8–10+ years of experience in Software Engineering, Data Science, Machine Learning, or AI
• 3+ years of experience as a Solution Architect/System Architect.
• Strong enterprise-level experience architecting and deploying GenAI solutions in large-scale production environments.
• Hands-on experience with Generative AI, LLMs, RAG, and Agentic AI/Multi-Agent architectures.
• Strong programming experience in Python.
• Experience with LangChain, LlamaIndex, CrewAI, AutoGen, or similar frameworks.
• Strong cloud architecture experience with Microsoft Azure or GCP.
• Hands-on experience with vector databases such as Pinecone, Milvus, Qdrant, or PGVector
• Strong understanding of MLOps/LLMOps, model deployment, monitoring, and productionization.
• Experience with SQL, PySpark, distributed computing, and API design.
• Experience with PyTorch or TensorFlow.

We are an equal opportunity employer. All aspects of employment including the decision to hire, promote, discipline, or discharge, will be based on merit, competence, performance, and business needs. We do not discriminate on the basis of race, color, religion, marital status, age, national origin, ancestry, physical or mental disability, medical condition, pregnancy, genetic information, gender, sexual orientation, gender identity or expression, national origin, citizenship/ immigration status, veteran status, or any other status protected under federal, state, or local law.