GenAI Architect for Enterprise—RAG, Agentic AI

KData AI; United States; Remote; USD 140,000 - 210,000; Full time

Job summary

KData AI is seeking an AI Architect to lead the architecture, design, and production rollout of enterprise‑grade GenAI solutions. The role focuses on GenAI, RAG, and agentic systems, translating business needs into scalable, secure architectures and governance standards.

The architect will partner with product, data, security, and engineering leaders to move AI capabilities—from experimentation to production—while ensuring observable, cost‑efficient and compliant delivery across teams.

Qualifications

• Demonstrated experience designing and deploying production‑grade GenAI applications such as conversational assistants and knowledge workers.
• Strong understanding of LLM behavior, prompting, and tool/function calling.
• Experience with inference and serving patterns for latency, throughput, and cost.
• Ability to evaluate build‑versus‑buy and hosted‑versus‑self‑hosted options.

Responsibilities

• Define enterprise AI architecture strategy and reference architectures aligned to business outcomes.
• Design end‑to‑end AI solutions spanning data ingestion, model access, retrieval, APIs, and deployment.
• Architect GenAI, RAG, and agentic workflows with quality, latency, and cost controls.
• Lead technical discovery, prototypes, and production readiness reviews; guide teams through implementation.

Skills

GenAI Architecture

RAG Systems

Agentic AI

Cloud Architecture

Python

Education

Bachelor's degree in Computer Science or related

Master's degree

Tools

LangChain

LangGraph

OpenAI

Mistral

TensorFlow

Job description

KData AI is seeking an AI Architect to lead the architecture, design, and production rollout of enterprise‑grade GenAI solutions. The role focuses on GenAI, RAG, and agentic systems, translating business needs into scalable, secure architectures and governance standards.

The architect will partner with product, data, security, and engineering leaders to move AI capabilities—from experimentation to production—while ensuring observable, cost‑efficient and compliant delivery across teams.