Generative AI Architect

Role: Generative AI Architect

Location: Plano, TX (Onsite)

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 (Google Cloud Platform).

·        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 Google Cloud Platform.

·        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.

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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.