AI Architect

Role overview

Architecture and delivery

• Assess current AI and analytics models, platforms, data, tools, use cases, integrations, and operational capabilities; define target architecture and transition roadmaps.
• Develop architecture principles, standards, patterns, reference architectures, and governance guardrails.
• Design ML, deep learning, GenAI, LLM, intelligent automation, and AI-enabled application architectures.
• Define platform architecture across development, training, deployment, inference, monitoring, and lifecycle management.
• Design GenAI patterns involving LLM integration, RAG, vector databases, prompt orchestration, guardrails, and knowledge integration.
• Define data architecture for preparation, feature engineering, feature stores, training data, metadata, and lineage; define MLOps and LLMOps lifecycle architecture.
• Design secure AI integrations with enterprise applications, APIs, data platforms, and external services.
• Embed responsible AI, privacy, security, explainability, transparency, fairness, and human oversight into designs.
• Define requirements for model monitoring, performance, drift, observability, auditability, and resilience; evaluate platforms, models, cloud services, and vendors.
• Collaborate with architecture, data, application, integration, cloud, security, and solution teams; support use-case assessment, governance reviews, and risk mitigation.

Experience

• Requires 8–10 years in technology, data, software engineering, analytics, machine learning, solution architecture, or a related discipline.
• Requires 3–5 years of hands-on AI/ML architecture, AI engineering, data science platforms, or comparable senior-role experience.
• Requires demonstrated enterprise-scale AI, ML, or GenAI solution design and experience with AI platforms, cloud AI services, model deployment, data pipelines, APIs, and enterprise integration.
• Generative AI/LLM architecture experience is highly desirable; enterprise transformation, innovation, analytics, or AI adoption experience is also requested.
• Experience working with senior stakeholders, data and engineering teams, cybersecurity, vendors, and governance functions.

Core competencies and technologies

• Core areas include enterprise AI, ML and GenAI/LLM architecture, AI platforms, MLOps/LLMOps, RAG, vector databases, semantic search, feature engineering, API integration, responsible AI, security, monitoring, cloud AI, and architecture governance.
• Preferred exposure includes Azure, AWS, Google Cloud or equivalent; foundation-model platforms; PyTorch, TensorFlow, scikit-learn or equivalent; MLOps tools; vector databases; data lakes, lakehouses and streaming; Docker and Kubernetes.
• Preferred architecture methods and tools include TOGAF, ArchiMate, cloud well-architected principles, data governance practices, API-first and event-driven patterns, Zero Trust, Bizzdesign, Sparx Enterprise Architect, Orbus/iServer, or ARIS.

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, or cloud architecture certification is highly desirable; TOGAF certification is desirable. Certifications in GenAI, cloud AI, data engineering, MLOps, or responsible AI are advantageous.

Personal attributes