Remote AI Solution Architect — Enterprise GenAI & LLMs

Ethereum Technologies

Tampa (FL)

Remote

USD 98,000 - 139,000

Full time

5 hours ago

Be an early applicant

A complete application in a minute

Tailored resume and cover letter, ready to send.

Get past ATS filters

Job summary

Ethereum Technologies seeks an experienced AI Solution Architect to design, develop, and implement scalable, secure, enterprise-grade AI solutions. You will lead architecture, guide technical implementations, and align AI initiatives with business objectives.

The role covers end-to-end AI/ML architecture, evaluation of major platforms (Azure OpenAI, AWS Bedrock, Vertex AI, OpenAI), and integration with enterprise systems. Strong Python, cloud, and security expertise are essential.

Qualifications

10+ years of experience in software engineering, solution architecture, or enterprise architecture with AI/ML exposure.
Strong experience in AI/ML architecture and Generative AI implementations.
Expertise in Large Language Models (LLMs), RAG, prompt engineering, and AI Agents.
Hands-on experience with Azure OpenAI, AWS Bedrock, Google Vertex AI, or OpenAI.
Strong programming experience in Python.
Experience with LangChain, LangGraph, LlamaIndex, or Semantic Kernel.
Knowledge of vector databases such as Pinecone, FAISS, ChromaDB, Weaviate, or Azure AI Search.
Experience designing REST APIs, microservices, and enterprise integrations.
Strong knowledge of cloud platforms (Azure/AWS/GCP).
Experience with Docker, Kubernetes, CI/CD, and cloud-native architecture.
Knowledge of MLOps/LLMOps, model monitoring, AI evaluation, and deployment pipelines.
Understanding of AI security, Responsible AI, data privacy, and governance.

Responsibilities

Design and architect end-to-end AI/ML and Generative AI solutions for enterprise applications.
Develop scalable AI architectures leveraging LLMs, RAG, AI Agents, and vector databases.
Define solution architecture, technical roadmaps, design patterns, and architecture standards.
Evaluate and implement AI platforms such as Azure OpenAI, AWS Bedrock, Google Vertex AI, and OpenAI.
Design and implement RAG pipelines, prompt engineering, embeddings, semantic search, and vector indexing.
Build and integrate AI-powered applications using Python, REST APIs, and microservices.
Work with frameworks such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, and Hugging Face.
Design AI agent workflows, tool calling, multi-agent orchestration, and MCP-based integrations.
Integrate AI solutions with enterprise systems, databases, APIs, and cloud platforms.
Ensure AI solutions meet enterprise security, scalability, performance, governance, and compliance requirements.
Establish MLOps and LLMOps practices for model deployment, monitoring, evaluation, and continuous improvement.
Collaborate with business stakeholders, data scientists, ML engineers, and development teams.