AI Architect
Role Overview
Defines and governs enterprise AI architecture for secure, scalable, reusable, business-aligned adoption of AI, machine learning, and generative AI.
Supports enterprise transformation through architecture strategy, platforms, model and data architecture, integrations, MLOps, responsible AI, and governance.
Key Skills for This Role
Full Job Posting
Role overview
Defines and governs enterprise AI architecture for secure, scalable, reusable, business-aligned adoption of AI, machine learning, and generative AI.
Supports enterprise transformation through architecture strategy, platforms, model and data architecture, integrations, MLOps, responsible AI, and governance.
Architecture responsibilities
- Assess the current AI and analytics landscape and define target-state architecture, roadmaps, principles, standards, patterns, and governance guardrails.
- Design AI, machine learning, deep learning, GenAI, LLM, intelligent automation, and AI-enabled application architectures.
- Define platform, data, MLOps, LLMOps, integration, monitoring, and operational resilience architecture.
- Embed security, privacy, responsible AI, explainability, fairness, transparency, and human oversight into designs.
- Evaluate platforms and technologies; collaborate with architecture teams and support use-case assessments, governance reviews, and risk mitigation.
Required experience
- 8–10 years in technology, data, software engineering, analytics, machine learning, solution architecture, or a related discipline.
- 3–5 years of hands-on AI/ML architecture, AI engineering, data science platform, or comparable senior experience.
- Demonstrated enterprise-scale AI, machine learning, or generative AI solution design experience.
- Experience with AI platforms, cloud AI services, model deployment, data pipelines, APIs, enterprise integration, and AI adoption programs.
- Experience collaborating with senior stakeholders, technical teams, vendors, and governance functions.
Core competencies
- Enterprise AI, machine learning, generative AI, LLM, and AI platform architecture.
- MLOps, LLMOps, RAG, vector databases, semantic search, data pipelines, and feature engineering.
- AI integration and APIs, responsible AI, governance, security, privacy, model monitoring, observability, and cloud AI architecture.
Preferred methods and technologies
Preferred methods include TOGAF, ArchiMate, cloud architecture principles, responsible AI practices, MLOps, data governance, API-first and event-driven patterns, and Zero Trust.
Preferred technology exposure includes Azure, AWS, Google Cloud or equivalents; LLM platforms; PyTorch, TensorFlow, or scikit-learn; MLOps tools; vector databases; data platforms; Docker; Kubernetes; and enterprise architecture tools.
Preferred qualifications
- A bachelor's degree in computer science, artificial intelligence, data science, software engineering, computer engineering, or a related discipline is preferred.
- Professional AI, machine learning, data, cloud architecture, TOGAF, generative AI, cloud AI, data engineering, MLOps, or responsible AI certifications are desirable or advantageous.
Personal attributes
Strong analytical, innovative, and systems-thinking capabilities; translates business opportunities into practical, governable AI architecture.
Communicates complex AI concepts clearly, facilitates stakeholder decisions, and focuses on security, responsible AI, data quality, scalability, and architecture quality.
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