Generative AI Architect

BlackCube Labs; Plano (TX); On-site; USD 150,000 - 230,000; Full time

Job summary

BlackCube Labs seeks a Generative AI Architect to design and implement enterprise-scale GenAI solutions in Plano, TX. The role focuses on LLMs, RAG, agentic AI, and scalable production deployments.

You will lead end-to-end GenAI projects, architect enterprise-grade systems, and collaborate with teams to ensure secure, scalable, and integrated AI workflows.

Qualifications

• 8-10+ years in Software Eng, Data Science, ML or AI.
• 3+ years as Solution Architect/System Architect.
• Experience deploying GenAI at scale in production.
• Hands-on GenAI, LLMs, RAG, and multi-agent tech.
• Strong Python programming.
• Experience with LangChain, LlamaIndex, CrewAI, AutoGen.
• Cloud architecture on Azure or GCP.
• Vector databases such as Pinecone, Milvus, Qdrant, or PGVector.
• MLOps/LLMOps, deployment, monitoring, productionization.
• SQL, PySpark, distributed computing, API design.
• 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+ yrs exp

Solution Architect

Python

LangChain

Azure / GCP

Vector DBs

MLOps/LLMOps

SQL PySpark

PyTorch / TF

APIs

Tools

LangChain

LlamaIndex

CrewAI

AutoGen

Pinecone

Milvus

Qdrant

PGVector

Job description

Title: Generative AI Architect

Location: Plano,TX

Experience: 8+ yrs

We are looking for an experienced and hands‑on Generative AI (GenAI) Architect to design and implement enterprise‑scale AI solutions. The ideal candidate will have strong experience with LLMs, Agentic AI, RAG, MLOps/LLMOps, cloud architecture, and enterprise production deployments.

The candidate must have proven experience taking GenAI solutions from prototype/POC to scalable enterprise production environments.

Key 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.

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.

Preferred Skills

• Experience designing and implementing multi‑agent AI ecosystems.
• Strong knowledge of enterprise AI security, governance, scalability, and integration patterns.
• Experience integrating GenAI/LLM solutions with existing enterprise applications and data platforms.