AI Architect

Job Title:

AI Architect

Location:

Remote

• 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
• Architect 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
• Define cloud-native AI platform strategies leveraging Azure, AWS, GCP, or hybrid environments
• 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

• 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

• Architect 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

• Define cloud-native AI platform strategies leveraging Azure, AWS, GCP, or hybrid environments
• 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

• 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
• Ensure architectures support responsible AI principles including transparency, accountability, and privacy

• Translate business requirements into technical architectures that stakeholders across business and IT can understand
• Guide cross-functional teams including data engineers, data scientists, and Dev Ops 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

• 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

• Enterprise AI Architecture – Proven ability to design scalable, production-grade AI architectures that…