What are the responsibilities and job description for the AI Architect position at Thinklusive?
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