AI Architect

Key Responsibilities

• Define enterprise AI reference architectures that enable rapid experimentation, scalable deployment, and long-term maintainability
• Design end-to-end AI solutions spanning data ingestion, model development, deployment, and monitoring layers
• Ensure architectural decisions balance innovation with enterprise standards for security, performance, and cost optimization
• Create reusable patterns, templates, and accelerators that reduce time-to-value for AI implementations

Data Architecture & MLOps Foundation

• Design data ecosystems that provide high-quality, accessible, and governed data for AI/ML workloads
• Design MLOps pipelines enabling continuous integration, delivery, and monitoring of AI models at scale
• Establish feature stores, model registries, and versioning frameworks that ensure reproducibility and traceability
• Optimize data architectures for real-time inference, batch processing, and hybrid deployment scenarios

Platform & Infrastructure Strategy

• Evaluate and recommend AI/ML platforms, tools, and frameworks aligned with client capabilities and objectives
• Design infrastructure architectures that optimize compute, storage, and networking for AI workloads
• Ensure platform choices support scalability, cost efficiency, and future technology evolution

AI Governance & Security Architecture

• Embed governance, security, and compliance requirements into AI architecture from design phase
• Design model explainability, bias detection, and fairness monitoring capabilities into solution architectures
• Architect audit trails, lineage tracking, and access controls that meet regulatory requirements
• Architectures support responsible AI principles including transparency, accountability, and privacy

Technical Leadership & Stakeholder Alignment

• Translate business requirements into technical architectures that stakeholders across business and IT can understand
• Guide cross-functional teams including data engineers, data scientists, and DevOps through implementation
• Influence enterprise architecture decisions to ensure AI readiness across technology landscape
• Serve as technical authority on AI initiatives, resolving design conflicts and ensuring architectural integrity

Innovation & Technology Roadmap

• Evaluate emerging AI technologies (GenAI, LLMs, edge AI) and assess applicability to client contexts
• Define technology roadmaps that evolve AI capabilities in alignment with business strategy
• Lead proof-of-concepts and technical pilots to validate architectural approaches before scale
• Contribute to intellectual property through reusable assets, frameworks, and technical publications

Preferred Skills

• Enterprise AI Architecture – Proven ability to design scalable, production-grade AI architectures that support diverse use cases from experimentation through enterprise-wide deployment with measurable business impact
• Data Engineering & MLOps Mastery – Deep expertise in architecting data pipelines, feature stores, model registries, and MLOps frameworks that enable continuous delivery and monitoring of AI models at scale
• Cloud & Platform Expertise – Extensive hands-on experience with cloud-native AI services across Azure (Azure ML, Synapse, Databricks), AWS (SageMaker, Bedrock), or GCP (Vertex AI) with ability to design hybrid and multi-cloud solutions
• Technical Leadership & Influence – Strong ability to lead technical teams, resolve complex design challenges, and communicate architectural decisions effectively to both technical and business stakeholders
• Governance & Security Integration – Experience embedding responsible AI principles, security controls, and regulatory compliance requirements into AI architectures from design through deployment

Qualifications

• 10-15 years of overall IT experience with minimum 8+ years focused on data engineering, AI/ML, and solution architecture
• 5+ years of experience designing and delivering enterprise-scale AI/ML and GenAI solutions in production environments
• Demonstrated track record of leading architecture for AI transformation programs across multiple industries
• Experience working with Fortune 500 or large enterprise clients in consulting or technology delivery roles
• Knowledge of AI Governance and Responsible & Ethical AI practices
• MBA preferred