AI Architect

Role: AI Architect

Location: Atlanta, GA

Experience: 15+ years

Summary

AI Architect is responsible for designing, governing, and guiding the architecture of AI and machine-learning solutions across the organization. This role focuses on enterprise-scale AI system design, ensuring AI solutions are scalable, secure, reliable, cost-effective, and aligned with business strategy and responsible AI principles.

Skills & Qualifications

• 15+ years in Architecture, digital transformation, data, or product/solution roles.
• 7+ years delivering AI or ML solutions at scale.
• Hands-on experience with at least one major cloud AI stack (Azure, AWS, or Google Cloud).

AI & Machine Learning Expertise

• Strong understanding of ML and GenAI concepts
  • Traditional ML, deep learning, LLMs, multimodal models
• Experience with AI frameworks
  • TensorFlow, PyTorch, scikit-learn, Hugging Face
• Knowledge of GenAI patterns
  • RAG, prompt engineering, fine-tuning, agents

Architecture & Software Engineering

• Deep expertise in distributed systems and microservices architecture
• API-based integration and event-driven architectures
• Strong system design and performance engineering skills

Data & MLOps

• Data architecture for structured and unstructured data
• MLOps lifecycle management (training, versioning, deployment, monitoring)
• CI/CD pipelines for AI systems

Cloud & Infrastructure

• Enterprise cloud architecture (Azure preferred)
• Containers and orchestration: Docker, Kubernetes
• Observability: logging, monitoring, tracing, model performance metrics

Security, Risk & Compliance

• AI security, model protection, data privacy, and IP safeguards
• Experience with governance frameworks and regulated environments
• Explainable AI and audit-ready solution design

Leadership & Communication

• Strong influence without direct authority
• Ability to guide multiple teams and initiatives
• Excellent communication with both technical and executive audiences

Responsibilities

AI Architecture & System Design

• Define end-to-end solution architectures, from data ingestion to model deployment and consumption
• Design scalable architectures for ML, GenAI, RAG systems, real-time inference, and batch prediction