Enterprise AI Architect: Platform & ML Systems

Appvion; Georgia; On-site; USD 140,000 - 210,000; Full time

Job summary

Appvion is seeking an AI Architect to define the technical foundation for AI/ML across the enterprise, aligning architecture standards, platform choices, and governance with business outcomes. You’ll bridge engineering, data, and strategy to accelerate AI adoption at scale.

You will design reference patterns, evaluate platforms, and lead MLOps practices while mentoring teams and staying ahead of trends to keep our AI roadmap secure, scalable, and compliant.

Qualifications

• 8 years in software or data architecture, with 4 years in ML systems
• Deep expertise in cloud platforms (AWS, Azure, or GCP) and their ML services
• Proven experience designing production ML pipelines at enterprise scale
• Strong understanding of MLOps, model monitoring, and deployment patterns
• Experience with both traditional ML and modern LLM/GenAI architectures
• Familiarity with core enterprise infrastructure architecture

Responsibilities

• Design the enterprise AI/ML architecture, including reference patterns and multi-tenant architectures
• Evaluate and select AI platforms, frameworks, and cloud services
• Establish technical standards for model development, testing, and deployment
• Design agentic search and retrieval systems for enterprise knowledge grounding
• Review and approve architecture for all AI use cases before they reach production
• Define data architecture requirements for ML pipelines
• Lead build vs. buy evaluations for AI tooling
• Mentor technical team members and drive engineering excellence
• Stay current on AI/ML technology trends and assess relevance to roadmap

Skills

Python

Scala

ML architecture

Cloud platforms

MLOps

LLM/GenAI

Data governance

Security by design

Tools

Docker

Kubernetes

CI/CD

MLflow

Model registries

Vector databases

Job description

Appvion is seeking an AI Architect to define the technical foundation for AI/ML across the enterprise, aligning architecture standards, platform choices, and governance with business outcomes. You’ll bridge engineering, data, and strategy to accelerate AI adoption at scale.

You will design reference patterns, evaluate platforms, and lead MLOps practices while mentoring teams and staying ahead of trends to keep our AI roadmap secure, scalable, and compliant.