AI Architect

The AI Architect is responsible for defining and governing enterprise artificial intelligence architecture to enable secure, scalable, reusable, and business-aligned adoption of AI, machine learning, and generative AI capabilities. The role covers AI strategy alignment, AI platforms, model and data architecture, integration patterns, MLOps, GenAI and LLM solutions, responsible AI controls, security, and architecture governance across enterprise transformation initiatives

Requirements

Develop and maintain the enterprise AI architecture in alignment with business strategy, enterprise architecture, data strategy, and digital transformation objectives.

· Assess the current AI and analytics landscape, including models, platforms, data sources, tools, use cases, integrations, and operational capabilities.

· Define target-state AI architecture and transition roadmaps for enterprise AI adoption.

· Develop AI architecture principles, standards, patterns, reference architectures, and governance guardrails.

· Design architectures for machine learning, deep learning, generative AI, large language models, intelligent automation, and AI-enabled applications.

· Define enterprise AI platform architecture covering development, training, deployment, inference, monitoring, and lifecycle management.

· Design GenAI architectures including LLM integration, retrieval-augmented generation, vector databases, prompt orchestration, guardrails, and knowledge integration.

· Define AI data architecture requirements including data preparation, feature engineering, feature stores, model training data, metadata, and lineage.

· Define MLOps and LLMOps architecture for model versioning, deployment, monitoring, retraining, evaluation, and governance.

· Design secure integration patterns between AI services, enterprise applications, APIs, data platforms, and external AI services.

· Embed responsible AI, privacy, security, explainability, transparency, fairness, and human oversight requirements into architecture designs.

· Define architecture requirements for model monitoring, performance, drift, observability, auditability, and operational resilience.

· Evaluate AI platforms, foundation models, cloud AI services, open-source technologies, and vendor solutions.

· Collaborate with enterprise, data, application, integration, cloud, security, and solution architects to ensure coherent end-to-end architectures.

· Support AI use-case assessment by evaluating feasibility, architecture complexity, data readiness, risk, scalability, and integration requirements.

· Participate in architecture governance, AI governance, technical design reviews, and Architecture Review Boards.

· Identify AI architecture risks, dependencies, constraints, ethical considerations, and mitigation actions.

Required Experience

· Minimum 8-10 years of experience in technology, data, software engineering, analytics, machine learning, solution architecture, or related disciplines.

· At least 3-5 years of hands-on experience in AI/ML architecture, AI engineering, data science platforms, or a comparable senior role.

· Demonstrated experience designing enterprise-scale AI, machine learning, or generative AI solutions.

· Experience with AI platforms, cloud AI services, model deployment, data pipelines, APIs, and enterprise integration.

· Experience with generative AI and large language model architectures is highly desirable.

· Experience supporting enterprise transformation, innovation, analytics, or AI adoption programs.

· Experience working with senior stakeholders, data teams, engineering teams, cybersecurity teams, vendors, and governance functions.

Core Competencies

· Enterprise AI Architecture

· Machine Learning Architecture

· Generative AI and LLM Architecture

· AI platform architecture

· MLOps and LLMOps

· Retrieval-Augmented Generation (RAG)

· Vector database and semantic search architecture

· AI data pipelines and feature engineering architecture

· AI integration and API architecture

· Responsible AI and AI governance

· AI security and privacy

· Model monitoring and observability

· Cloud AI architecture

· Architecture governance and assurance

Preferred Frameworks, Standards & Methods

· TOGAF

· ArchiMate

· Cloud architecture and AI well-architected principles

· Responsible AI principles and governance practices

· MLOps and model lifecycle management practices

· Data governance and metadata management practices

· API-first and event-driven integration patterns

· Zero Trust and secure-by-design principles for AI environments

Preferred Tools & Technology Exposure

· AI and machine learning services across Microsoft Azure, AWS, Google Cloud, or equivalent platforms.

· Generative AI and foundation model platforms, including enterprise LLM services and model orchestration technologies.

· Machine learning frameworks such as PyTorch, TensorFlow, scikit-learn, or equivalent.

· MLOps platforms and model lifecycle management tools.

· Vector databases, semantic search, embeddings, and Retrieval-Augmented Generation technologies.

· Data platforms, data lakes, lakehouses, streaming, and analytics technologies.

· Containerization and orchestration technologies such as Docker and Kubernetes.

· Enterprise architecture tools such as Bizzdesign, Sparx Enterprise Architect, Orbus/iServer, ARIS, or similar platforms.

Preferred Qualifications & Certifications

· Bachelor's degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering, Computer Engineering, or a related discipline.

· Professional AI, machine learning, data, or cloud architecture certification is highly desirable.

· TOGAF certification is desirable.

· Relevant certifications in generative AI, cloud AI, data engineering, MLOps, or responsible AI are advantageous.

Personal Attributes

· Strong analytical, innovative, and systems-thinking capability.

· Ability to translate business opportunities into practical and governable AI architecture.

· Strong communication, facilitation, and stakeholder management skills.

· Ability to explain complex AI concepts clearly to both technical and non-technical stakeholders.

· Strong focus on security, responsible AI, data quality, scalability, and architecture quality.

· Consulting mindset with the ability to evaluate emerging technologies objectively and support enterprise decision-making.