Enterprise Architect
- TEKsystems
- Lincolnshire, Illinois
- 08/23/2026
Full time
Information Technology
Telecommunications
Management
Job Description
Job Description Top Skills:
Must have experience with
The AI Enterprise Architect is responsible for defining, evolving, and driving the enterprise architecture for artificial intelligence across the environment.
This is not a theoretical, advisory-only, or documentation-focused architecture role. The AI Enterprise Architect must be a hands-on technology leader who can move rapidly from an ambiguous business problem to an executable architecture, working reference implementation, production deployment, and measurable business outcome.
The role operates at the intersection of enterprise architecture, AI engineering, data, security, cloud platforms, integration, product delivery, and business strategy. This individual will establish the enterprise direction for generative AI, agentic AI, machine learning, intelligent automation, AI-enabled applications, and shared AI platform capabilities.
The AI Enterprise Architect will work within a Fortune 500 environment while supporting an AI program that operates with the urgency, experimentation, adaptability, and delivery expectations of a startup. The successful candidate must be comfortable making architecture decisions in a rapidly evolving technology landscape, challenging conventional approaches, eliminating unnecessary complexity, and personally driving initiatives through organizational and technical barriers.
This individual must be capable of seeing the entire enterprise AI ecosystem while remaining close enough to implementation to validate architectures, examine code and configurations, build prototypes, identify delivery risks, and distinguish production-ready capabilities from demonstrations and vendor claims.
Key Responsibilities
Enterprise AI Strategy and Target Architecture
• Define and maintain the enterprise AI target architecture, transition architectures, capability model, platform strategy, and multiyear architecture roadmap.
• Translate business strategy and operating priorities into executable AI capabilities, architecture investments, and delivery sequences.
• Establish the architectural direction for generative AI, agentic AI, machine learning, intelligent automation, AI-assisted decision-making, and AI-enabled business processes.
• Define clear boundaries and relationships between enterprise AI platforms, domain solutions, shared services, data platforms, enterprise applications, and external AI providers.
• Ensure that project-level AI decisions support enterprise scalability, interoperability, security, reuse, and long-term maintainability.
• Identify opportunities to consolidate overlapping technologies, eliminate duplicated capabilities, and prevent uncontrolled AI platform and vendor sprawl.
• Develop architecture options and recommendations that explicitly address business value, delivery speed, cost, risk, technical debt, vendor dependency, and operational complexity.
• Maintain a current enterprise view of AI capabilities, platforms, models, agents, integrations, data dependencies, vendors, risks, and strategic initiatives.
Hands-On AI Architecture and Delivery
• Lead AI initiatives from problem definition and architecture through implementation, production deployment, adoption, and measurable outcomes.
• Develop working prototypes and reference implementations to validate architecture decisions, platform capabilities, integration approaches, security controls, and delivery feasibility.
• Review source code, prompts, agent definitions, tool configurations, retrieval pipelines, model configurations, APIs, infrastructure, and deployment pipelines as needed to validate solution quality.
• Work directly with engineering teams to resolve architecture and implementation issues rather than limiting involvement to reviews or recommendations.
• Rapidly diagnose delivery blockers, simplify overengineered approaches, reduce unnecessary scope, and establish practical paths to production.
• Define production-readiness criteria and ensure that AI solutions meet requirements for reliability, security, performance, observability, supportability, cost, and business continuity.
• Distinguish clearly between proof of concept, pilot, minimum viable product, production capability, and enterprise platform.
• Remain personally accountable for architecture outcomes, not only architecture artifacts or review completion.
Generative and Agentic AI Architecture
• Design enterprise-grade architectures for large language models, multimodal models, AI assistants, autonomous and semi-autonomous agents, and AI-enabled applications.
• Define patterns for single-agent and multi-agent orchestration, tool use, planning, reasoning, memory, state management, delegation, and human approval.
• Establish architecture standards for retrieval-augmented generation, structured retrieval, knowledge graphs, semantic search, and enterprise knowledge access.
• Define patterns for context engineering, prompt management, structured outputs, model routing, fallback, caching, and workload segmentation.
• Architect secure agent access to enterprise systems, APIs, data, workflows, and external services.
• Define patterns for Model Context Protocol, agent-to-agent communication, enterprise APIs, event-driven interactions, and tool integration.
• Establish controls around nondeterministic model behavior, including deterministic validation, approval checkpoints, execution boundaries, and exception handling.
• Evaluate when AI agents are appropriate and when conventional software, workflow automation, rules engines, APIs, or analytics provide a better solution.
• Prevent the use of generative AI or agents where the architecture introduces unnecessary cost, risk, latency, or operational complexity.
Enterprise AI Platform Architecture
• Define the architecture for shared enterprise AI platform capabilities, including model access, model gateways, agent runtime services, retrieval services, evaluation services, security controls, observability, and cost management.
• Establish reusable AI services, platform components, reference architectures, templates, development patterns, and deployment patterns.
• Define enterprise model access, model selection, model portability, workload routing, quota management, and vendor abstraction strategies.
• Design workload, tenant, domain, environment, and data isolation patterns appropriate to enterprise risk and operating requirements.
• Establish architectural standards for proprietary, open-weight, hosted, and internally operated models.
• Define integration patterns between AI platforms and enterprise cloud, data, identity, security, integration, application, and observability platforms.
• Partner with platform engineering, cloud infrastructure, data, cybersecurity, and application teams to establish a scalable AI operating environment.
• Ensure that platform capabilities are implemented as usable products and services rather than architecture concepts that delivery teams cannot practically adopt.
Data and Knowledge Architecture
• Define data and knowledge architecture required to support AI models, agents, applications, evaluation, analytics, and business processes.
• Establish patterns for structured, semi-structured, and unstructured data access.
• Define architectures using relational, document, graph, vector, search, streaming, and analytical technologies based on workload requirements.
• Establish standards for embeddings, chunking, indexing, metadata, reranking, retrieval, source attribution, and information freshness.
• Define approaches for enterprise taxonomies, ontologies, semantic models, knowledge graphs, and reusable domain knowledge.
• Ensure appropriate data lineage, provenance, ownership, quality, classification, access control, retention, and usage restrictions.
• Define requirements for training, fine-tuning, inference, retrieval, evaluation, monitoring, and feedback datasets.
• Ensure that AI responses and actions can be traced to authoritative enterprise information where required.
• Identify situations where weak data, fragmented ownership, or poor knowledge management must be corrected rather than hidden behind an AI interface.
Integration and Distributed Systems Architecture
• Define and enforce AI integration patterns across enterprise applications, cloud platforms, SaaS products, data platforms, APIs, workflows, and external services.
• Architect synchronous and asynchronous APIs, event-driven interactions, messaging, streaming, workflow orchestration, and long-running business processes.
• Establish standards for identity propagation, delegated authorization, agent identity, workload identity, and service-to-service authentication.
• Define system-of-record ownership . click apply for full job details
- Enterprise AI Architecture (Model gateways, Model routing, Shared AI services, Platform engineering, AI service abstraction layers, Multi-model strategies, Vendor portability, AI observability, Platform scalability, Tenant isolation, AI FinOps and cost controls)
- RAG and Knowledge Architecture
- Generative AI & Agentic AI Architecture
Must have experience with
- Enterprise AI Platforms
- Enterprise Standards and Patterns
- Enterprise AI Strategy
- Target-State Architecture
- Architecture Governance
- Executive Influence
The AI Enterprise Architect is responsible for defining, evolving, and driving the enterprise architecture for artificial intelligence across the environment.
This is not a theoretical, advisory-only, or documentation-focused architecture role. The AI Enterprise Architect must be a hands-on technology leader who can move rapidly from an ambiguous business problem to an executable architecture, working reference implementation, production deployment, and measurable business outcome.
The role operates at the intersection of enterprise architecture, AI engineering, data, security, cloud platforms, integration, product delivery, and business strategy. This individual will establish the enterprise direction for generative AI, agentic AI, machine learning, intelligent automation, AI-enabled applications, and shared AI platform capabilities.
The AI Enterprise Architect will work within a Fortune 500 environment while supporting an AI program that operates with the urgency, experimentation, adaptability, and delivery expectations of a startup. The successful candidate must be comfortable making architecture decisions in a rapidly evolving technology landscape, challenging conventional approaches, eliminating unnecessary complexity, and personally driving initiatives through organizational and technical barriers.
This individual must be capable of seeing the entire enterprise AI ecosystem while remaining close enough to implementation to validate architectures, examine code and configurations, build prototypes, identify delivery risks, and distinguish production-ready capabilities from demonstrations and vendor claims.
Key Responsibilities
Enterprise AI Strategy and Target Architecture
• Define and maintain the enterprise AI target architecture, transition architectures, capability model, platform strategy, and multiyear architecture roadmap.
• Translate business strategy and operating priorities into executable AI capabilities, architecture investments, and delivery sequences.
• Establish the architectural direction for generative AI, agentic AI, machine learning, intelligent automation, AI-assisted decision-making, and AI-enabled business processes.
• Define clear boundaries and relationships between enterprise AI platforms, domain solutions, shared services, data platforms, enterprise applications, and external AI providers.
• Ensure that project-level AI decisions support enterprise scalability, interoperability, security, reuse, and long-term maintainability.
• Identify opportunities to consolidate overlapping technologies, eliminate duplicated capabilities, and prevent uncontrolled AI platform and vendor sprawl.
• Develop architecture options and recommendations that explicitly address business value, delivery speed, cost, risk, technical debt, vendor dependency, and operational complexity.
• Maintain a current enterprise view of AI capabilities, platforms, models, agents, integrations, data dependencies, vendors, risks, and strategic initiatives.
Hands-On AI Architecture and Delivery
• Lead AI initiatives from problem definition and architecture through implementation, production deployment, adoption, and measurable outcomes.
• Develop working prototypes and reference implementations to validate architecture decisions, platform capabilities, integration approaches, security controls, and delivery feasibility.
• Review source code, prompts, agent definitions, tool configurations, retrieval pipelines, model configurations, APIs, infrastructure, and deployment pipelines as needed to validate solution quality.
• Work directly with engineering teams to resolve architecture and implementation issues rather than limiting involvement to reviews or recommendations.
• Rapidly diagnose delivery blockers, simplify overengineered approaches, reduce unnecessary scope, and establish practical paths to production.
• Define production-readiness criteria and ensure that AI solutions meet requirements for reliability, security, performance, observability, supportability, cost, and business continuity.
• Distinguish clearly between proof of concept, pilot, minimum viable product, production capability, and enterprise platform.
• Remain personally accountable for architecture outcomes, not only architecture artifacts or review completion.
Generative and Agentic AI Architecture
• Design enterprise-grade architectures for large language models, multimodal models, AI assistants, autonomous and semi-autonomous agents, and AI-enabled applications.
• Define patterns for single-agent and multi-agent orchestration, tool use, planning, reasoning, memory, state management, delegation, and human approval.
• Establish architecture standards for retrieval-augmented generation, structured retrieval, knowledge graphs, semantic search, and enterprise knowledge access.
• Define patterns for context engineering, prompt management, structured outputs, model routing, fallback, caching, and workload segmentation.
• Architect secure agent access to enterprise systems, APIs, data, workflows, and external services.
• Define patterns for Model Context Protocol, agent-to-agent communication, enterprise APIs, event-driven interactions, and tool integration.
• Establish controls around nondeterministic model behavior, including deterministic validation, approval checkpoints, execution boundaries, and exception handling.
• Evaluate when AI agents are appropriate and when conventional software, workflow automation, rules engines, APIs, or analytics provide a better solution.
• Prevent the use of generative AI or agents where the architecture introduces unnecessary cost, risk, latency, or operational complexity.
Enterprise AI Platform Architecture
• Define the architecture for shared enterprise AI platform capabilities, including model access, model gateways, agent runtime services, retrieval services, evaluation services, security controls, observability, and cost management.
• Establish reusable AI services, platform components, reference architectures, templates, development patterns, and deployment patterns.
• Define enterprise model access, model selection, model portability, workload routing, quota management, and vendor abstraction strategies.
• Design workload, tenant, domain, environment, and data isolation patterns appropriate to enterprise risk and operating requirements.
• Establish architectural standards for proprietary, open-weight, hosted, and internally operated models.
• Define integration patterns between AI platforms and enterprise cloud, data, identity, security, integration, application, and observability platforms.
• Partner with platform engineering, cloud infrastructure, data, cybersecurity, and application teams to establish a scalable AI operating environment.
• Ensure that platform capabilities are implemented as usable products and services rather than architecture concepts that delivery teams cannot practically adopt.
Data and Knowledge Architecture
• Define data and knowledge architecture required to support AI models, agents, applications, evaluation, analytics, and business processes.
• Establish patterns for structured, semi-structured, and unstructured data access.
• Define architectures using relational, document, graph, vector, search, streaming, and analytical technologies based on workload requirements.
• Establish standards for embeddings, chunking, indexing, metadata, reranking, retrieval, source attribution, and information freshness.
• Define approaches for enterprise taxonomies, ontologies, semantic models, knowledge graphs, and reusable domain knowledge.
• Ensure appropriate data lineage, provenance, ownership, quality, classification, access control, retention, and usage restrictions.
• Define requirements for training, fine-tuning, inference, retrieval, evaluation, monitoring, and feedback datasets.
• Ensure that AI responses and actions can be traced to authoritative enterprise information where required.
• Identify situations where weak data, fragmented ownership, or poor knowledge management must be corrected rather than hidden behind an AI interface.
Integration and Distributed Systems Architecture
• Define and enforce AI integration patterns across enterprise applications, cloud platforms, SaaS products, data platforms, APIs, workflows, and external services.
• Architect synchronous and asynchronous APIs, event-driven interactions, messaging, streaming, workflow orchestration, and long-running business processes.
• Establish standards for identity propagation, delegated authorization, agent identity, workload identity, and service-to-service authentication.
• Define system-of-record ownership . click apply for full job details
About TEKsystems