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

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.

• 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.

• Enterprise AI Architecture
• Machine Learning Architecture
• Generative AI and LLM Architecture
• AI platform architecture
• MLOps and LLMOps
• Retrieval-Augmented Generation (RAG)
• Vector…