AI Architect

Role Overview

The AI Architect will lead the definition and governance of the organization's enterprise artificial intelligence architecture. This strategic role ensures the secure, scalable, reusable, and business-aligned integration of AI, machine learning, and generative AI technologies into enterprise operations and transformations.

Key Responsibilities

• Develop and maintain enterprise AI architecture in harmony with business, enterprise, data strategies, and digital transformation goals.
• Evaluate the current AI and analytics ecosystem, including models, platforms, data sources, operational capabilities, and integrations.
• Establish target AI architecture and develop transition roadmaps to support enterprise-wide AI adoption.
• Create AI architecture principles, standards, reference models, governance frameworks, and best practices.
• Architect solutions involving machine learning, deep learning, generative AI, large language models, intelligent automation, and AI-driven applications.
• Design holistic AI platform architectures covering development, training, deployment, inference, monitoring, and lifecycle management.
• Build generative AI frameworks that incorporate LLM integration, retrieval-augmented generation, vector databases, prompt orchestration, knowledge bases, and guardrails.
• Specify AI data architecture needs including data prep, feature engineering, feature stores, metadata, lineage, and model training data.
• Define MLOps and LLMOps frameworks to handle versioning, deployment, monitoring, retraining, evaluation, and governance.
• Develop secure integration patterns linking AI services with enterprise applications, APIs, data platforms, and external AI systems.
• Incorporate responsible AI practices addressing privacy, security, explainability, transparency, fairness, and ensuring human oversight within architecture designs.
• Set architecture requirements for monitoring model performance, drift detection, observability, auditability, and operational resilience.
• Analyze and evaluate AI platforms, foundational models, cloud AI services, open-source tooling, and vendor products.
• Collaborate cross-functionally with enterprise, data, solution, application, integration, cloud, security architects to build cohesive end-to-end AI architectures.
• Assist in AI use case assessments by reviewing feasibility, complexity, data readiness, risk, scalability, and integration challenges.
• Engage in architecture governance activities including AI governance, technical design reviews, and participation in Architecture Review Boards.
• Identify and mitigate AI architecture risks, constraints, dependencies, and ethical considerations.

Experience Requirements

• At least 8 to 10 years working in technology domains such as data, software engineering, analytics, machine learning, solution architecture, or related fields.
• Minimum 3 to 5 years of hands-on experience specifically in AI/ML architecture, AI engineering, data science platforms, or similar leadership roles.
• Proven track record designing enterprise-scale AI, machine learning, or generative AI solutions.
• Experience with AI platforms, cloud AI services, model deployment workflows, data pipelines, APIs, and enterprise integration.
• Specialized experience with generative AI and large language model architectures is strongly preferred.
• Background supporting enterprise-wide transformation, innovation, analytics, or AI adoption initiatives.
• Skilled in collaborating with senior executives, data and engineering teams, cybersecurity, vendors, and governance stakeholders.

Core Expertise

• Enterprise AI architecture design
• Machine learning systems architecture
• Generative AI and large language model integration
• AI platform structure and management
• MLOps and LLMOps lifecycle methodologies
• Retrieval-Augmented Generation (RAG) strategies
• Vector database and semantic search frameworks
• AI data pipelines and feature engineering processes
• AI integration, API architecture, and security
• Responsible AI implementation and governance
• AI security and privacy considerations
• Model monitoring and observability techniques
• Cloud-based AI architecture
• Architecture governance and assurance mechanisms

Preferred Frameworks and Standards

• TOGAF and ArchiMate for enterprise architecture
• Cloud and AI well-architected principles
• Responsible AI governance frameworks
• MLOps and model lifecycle best practices
• Data governance and metadata management
• API-first and event-driven integration patterns
• Zero Trust and secure-by-design AI environment principles

Technology and Tools

• AI and machine learning services on Microsoft Azure, AWS, Google Cloud, or similar platforms
• Generative AI platforms, enterprise LLM services, and model orchestration technology
• Machine learning frameworks like PyTorch, TensorFlow, scikit-learn, or equivalents
• MLOps platforms and tools supporting model lifecycle management
• Vector databases, semantic search, embedding techniques, RAG technologies
• Data platforms including lakes, lakehouses, streaming and analytics tools
• Containerization and orchestration with Docker, Kubernetes
• Enterprise architecture software such as Bizzdesign, Sparx EA, Orbus/iServer, ARIS or similar

Qualifications and Certifications

• Bachelor’s degree in Computer Science, AI, Data Science, Software Engineering, Computer Engineering, or related disciplines
• Professional certifications in AI, machine learning, data architecture, cloud architecture preferred
• TOGAF certification is a plus
• Certifications related to generative AI, cloud AI, data engineering, MLOps, or responsible AI highly advantageous

Personal Attributes

• Strong analytical and systems-thinking abilities
• Capable of converting business opportunities into effective, governable AI solutions
• Excellent communication and stakeholder management skills
• Ability to clearly articulate complex AI concepts to both technical and non-technical audiences
• Focus on security, responsible AI practices, data quality, scalability, and architectural quality
• Consultative approach with aptitude for evaluating emerging technologies and informing enterprise decisions