AI Security Architect

Nexwave; Plano (TX); On-site; USD 180,000 - 250,000; Full time

Job summary

Nexwave seeks an experienced Generative AI Architect to design and implement enterprise-scale GenAI solutions. You will lead end-to-end GenAI and Agentic AI initiatives, architecting on Azure or GCP and driving production deployments.

You will work across data platforms, APIs, and cloud services, establishing MLOps/LLMOps, governance, and reliability in enterprise environments. Strong Python and distributed computing skills are essential.

Qualifications

• 8-10+ years in software eng, data science, ML or AI.
• 3+ years as a Solution Architect/System Architect.
• Proven GenAI deployments in large-scale production.
• Strong Python programming.
• Experience with LangChain, LlamaIndex, CrewAI, AutoGen or similar.
• Cloud architecture on Azure or GCP.
• Experience with vector databases (e.g., Pinecone, Milvus, Qdrant, PGVector).
• Strong MLOps/LLMOps deployment and monitoring know-how.
• SQL, PySpark, distributed computing, and API design.

Responsibilities

• Architect and implement end-to-end Generative AI and Agentic AI solutions.
• Design enterprise-grade GenAI 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

GenAI Architecting

Python

LangChain/LlamaIndex

Cloud (Azure/GCP)

Vector DBs

MLOps/LLMOps

SQL/PySpark

Job description

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.