Generative AI Architect

Cloud Analytics Technologies, LLC; Plano (TX); On-site; USD 170,000 - 210,000; Full time

Job summary

Cloud Analytics Technologies, LLC seeks an experienced architect to lead end-to-end Generative AI and Agentic AI initiatives. You will design enterprise-grade GenAI solutions using LLMs, RAG, multi-agent workflows, and vector databases, and build scalable architectures on Azure or GCP.

You will drive GenAI from POC to production, integrate with enterprise applications and data platforms, and establish MLOps/LLMOps practices to ensure secure, reliable deployments across a large organization.

Qualifications

• 8–10+ years of experience in software engineering, data science, ML, or AI.
• 3+ years as a Solution Architect/System Architect.
• Experience deploying GenAI solutions in large-scale production.
• Hands-on with Generative AI, LLMs, RAG, and multi-agent architectures.
• Proficient in Python.
• Experience with LangChain, LlamaIndex, CrewAI, AutoGen, or similar frameworks.
• Cloud architecture experience with Azure or GCP.
• Hands-on with vector databases (Pinecone, Milvus, Qdrant, PGVector).
• Understanding of MLOps/LLMOps, deployment and monitoring.
• Experience with SQL, PySpark, distributed computing, API design.
• Experience with PyTorch or TensorFlow.

Responsibilities

• 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.

Skills

8–10+ years experience

Solution Architect/System Architect

GenAI large-scale deployments

Generative AI, LLMs, RAG, agentic AI

Python

LangChain/LlamaIndex/CrewAI/AutoGen

Azure or GCP

Vector databases (Pinecone Milvus Qdr?

MLOps/LLMOps

SQL PySpark API design

PyTorch or TensorFlow

Job description

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.

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