72 jobs found

Job Description Top Skills: 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 previous experience as an Architect implementing A.I into an Enterprise Environment Must have experience with Enterprise AI Platforms Enterprise Standards and Patterns Enterprise AI Strategy Target-State Architecture Architecture Governance Executive Influence Description 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

Job Description Top Skills' Details AI solution architecture across business, application, data, integration, security, and infrastructure domains Generative AI, RAG, and Agentic AI Architecture (Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Agentic AI and multi-agent systems, Prompt engineering and context orchestration, Vector databases, embeddings, reranking, and retrieval strategies, Model selection, routing, fallback, and optimization. AI Security, Identity, and Governance Must Have previous experience as an Architect with A.I Enterprise Experience Must have For the AI Solution Architect role, Generative AI RAG / knowledge architecture Agent architecture AI evaluation AI security LLMOps / AgentOps and production operations Model architecture Enterprise integration AI economics Description The AI Solutions Architect serves as the technical architecture leader for enterprise AI solutions within the AI Center of Excellence and reports directly to the Vice President of Enterprise Architecture. This role is responsible for designing secure, scalable, supportable, and economically sustainable AI-enabled solutions from initial concept through production deployment and ongoing operation. The AI Solutions Architect owns the end-to-end architecture of assigned AI initiatives, including business requirements, artificial intelligence models, agents, data and knowledge sources, integrations, identity, security, infrastructure, observability, operational support, and governance controls. The role ensures that AI capabilities are not developed as isolated experiments, but as enterprise-grade solutions that integrate with existing business processes, platforms, applications, and data ecosystems. The architect works directly with business stakeholders, product owners, engineering teams, Cybersecurity, Data and Analytics, Infrastructure, Legal, Risk, and vendor partners while ensuring alignment with enterprise architecture strategy, standards, governance, and technology roadmaps. The individual must be able to translate business objectives into actionable architecture, validate technical designs through hands-on analysis and prototyping, and clearly communicate architectural decisions, risks, costs, and trade-offs. This position requires demonstrated experience delivering production generative AI, retrieval-augmented generation, machine learning, and agentic AI solutions. Experience limited to strategy, presentations, vendor demonstrations, or proofs of concept does not satisfy the requirements of the role. AI Solution Architecture • Design end-to-end architectures for enterprise AI solutions, including generative AI, retrieval-augmented generation, conversational AI, predictive machine learning, intelligent automation, computer vision, speech, and agentic AI capabilities. • Translate business requirements into comprehensive technical solution designs covering applications, models, agents, data, integrations, security, cloud infrastructure, observability, operations, and governance. • Determine whether artificial intelligence is appropriate for a given business problem and recommend alternative technical approaches when AI does not provide sufficient value, reliability, or economic benefit. • Define current-state, target-state, and transitional architectures for AI initiatives, including technical dependencies, shared capabilities, implementation phases, and architecture risks. • Ensure AI solutions align with enterprise architecture standards, cloud strategies, approved technology platforms, cybersecurity requirements, data governance policies, and operational support models. • Create architecture acceptance criteria and validate that proposed solutions meet functional, technical, security, operational, and business requirements before production deployment. Generative AI and Model Architecture • Design production-grade generative AI solutions using commercial, open-source, hosted, dedicated, and privately deployed models. • Evaluate and select language, vision, speech, embedding, reranking, and multimodal models based on solution quality, latency, cost, context requirements, data sensitivity, deployment options, scalability, supportability, and vendor risk. • Design multi-model architectures that support model routing, fallback, portability, workload specialization, and reduced dependency on a single model provider. • Define prompt architecture, context assembly, structured-output requirements, response validation, model fallback, caching, rate limiting, and error-handling patterns. • Evaluate model performance using repeatable technical and business criteria rather than vendor benchmarks or demonstration results alone. • Maintain awareness of model capabilities, limitations, licensing considerations, deployment constraints, and rapidly changing AI platform capabilities. Agentic AI Architecture • Design secure and reliable AI agents that can reason, use tools, maintain state, interact with enterprise applications, and execute controlled business workflows. • Define agent responsibilities, tool boundaries, memory models, workflow states, delegation rules, approval requirements, and termination conditions. • Design single-agent and multi-agent solutions using deterministic workflow controls around nondeterministic model behavior. • Establish architecture patterns for human-in-the-loop review, escalation, exception handling, retries, timeouts, circuit breakers, compensating actions, and emergency termination. • Define secure agent-to-user, agent-to-agent, and agent-to-tool interaction patterns. • Prevent uncontrolled agent autonomy by enforcing least privilege, constrained tool access, transaction limits, validation rules, and approval gates for consequential actions. • Partner with AI engineering teams to establish consistent agent development, orchestration, testing, deployment, and lifecycle management standards. Retrieval-Augmented Generation and Knowledge Architecture • Design enterprise retrieval-augmented generation solutions across structured, unstructured, document, transactional, graph, and operational data sources. • Define document ingestion, parsing, chunking, metadata enrichment, embedding, indexing, retrieval, reranking, citation, and knowledge-refresh strategies. • Design lexical, semantic, vector, hybrid, graph-enhanced, and structured retrieval patterns based on the characteristics of each use case. • Ensure retrieval solutions preserve source-system security, authorization, data classification, retention, and user entitlements. • Establish patterns for authorization-aware retrieval, source attribution, content freshness, provenance, and deletion. • Define controls for retrieval poisoning, outdated content, duplicate information, conflicting sources, inappropriate data exposure, and unsupported model responses. • Evaluate retrieval quality, answer relevance, groundedness, citation accuracy, and knowledge coverage before production deployment. AI Evaluation and Quality Engineering • Define measurable quality standards and evaluation strategies for generative AI, retrieval, machine learning, and agentic AI solutions. • Establish golden datasets, benchmark scenarios, regression suites, adversarial tests, and business acceptance criteria. • Define evaluation methods for accuracy, relevance, groundedness, hallucination, toxicity, bias, safety, retrieval quality, tool selection, tool-call accuracy, agent trajectory, and task completion. • Implement automated evaluation gates within AI development and deployment pipelines. • Define the appropriate use of human evaluation, expert review, LLM-based evaluation, deterministic testing, and statistical analysis. • Ensure model, prompt, retrieval, agent, and tool changes are tested against previous production behavior before release. • Establish production quality thresholds, monitoring requirements, rollback criteria, and exception-management processes. AI Security, Identity, and Trust Architecture • Design AI solutions in accordance with enterprise cybersecurity, privacy, identity, compliance, and risk-management requirements. • Perform AI-specific threat modeling covering prompt injection, indirect prompt injection, data poisoning, retrieval poisoning, sensitive-data exposure, model extraction, system-prompt leakage, insecure tool invocation, excessive agency, and downstream code execution. • Define identity propagation and authorization patterns across users, agents, models, tools, APIs, applications, data sources, and external services. • Design least-privilege access, workload identities, delegated authorization, service accounts, session isolation, tenant isolation, and approval controls. • Ensure agents cannot access data, tools, or transactions beyond the permissions of the requesting user or approved system identity. • Define security controls for AI gateways, model endpoints, vector stores, knowledge bases, MCP servers, external tools, plugins, third-party models, and vendor services. • Establish auditability that records who initiated an AI request, what context was used, which decisions were made, which tools were invoked, who approved an action, and what action was executed. . click apply for full job details

Job Description Top Skills' Details AI solution architecture across business, application, data, integration, security, and infrastructure domains Generative AI, RAG, and Agentic AI Architecture (Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Agentic AI and multi-agent systems, Prompt engineering and context orchestration, Vector databases, embeddings, reranking, and retrieval strategies, Model selection, routing, fallback, and optimization. AI Security, Identity, and Governance Must Have previous experience as an Architect with A.I Enterprise Experience Must have For the AI Solution Architect role, Generative AI RAG / knowledge architecture Agent architecture AI evaluation AI security LLMOps / AgentOps and production operations Model architecture Enterprise integration AI economics Description The AI Solutions Architect serves as the technical architecture leader for enterprise AI solutions within the AI Center of Excellence and reports directly to the Vice President of Enterprise Architecture. This role is responsible for designing secure, scalable, supportable, and economically sustainable AI-enabled solutions from initial concept through production deployment and ongoing operation. The AI Solutions Architect owns the end-to-end architecture of assigned AI initiatives, including business requirements, artificial intelligence models, agents, data and knowledge sources, integrations, identity, security, infrastructure, observability, operational support, and governance controls. The role ensures that AI capabilities are not developed as isolated experiments, but as enterprise-grade solutions that integrate with existing business processes, platforms, applications, and data ecosystems. The architect works directly with business stakeholders, product owners, engineering teams, Cybersecurity, Data and Analytics, Infrastructure, Legal, Risk, and vendor partners while ensuring alignment with enterprise architecture strategy, standards, governance, and technology roadmaps. The individual must be able to translate business objectives into actionable architecture, validate technical designs through hands-on analysis and prototyping, and clearly communicate architectural decisions, risks, costs, and trade-offs. This position requires demonstrated experience delivering production generative AI, retrieval-augmented generation, machine learning, and agentic AI solutions. Experience limited to strategy, presentations, vendor demonstrations, or proofs of concept does not satisfy the requirements of the role. AI Solution Architecture • Design end-to-end architectures for enterprise AI solutions, including generative AI, retrieval-augmented generation, conversational AI, predictive machine learning, intelligent automation, computer vision, speech, and agentic AI capabilities. • Translate business requirements into comprehensive technical solution designs covering applications, models, agents, data, integrations, security, cloud infrastructure, observability, operations, and governance. • Determine whether artificial intelligence is appropriate for a given business problem and recommend alternative technical approaches when AI does not provide sufficient value, reliability, or economic benefit. • Define current-state, target-state, and transitional architectures for AI initiatives, including technical dependencies, shared capabilities, implementation phases, and architecture risks. • Ensure AI solutions align with enterprise architecture standards, cloud strategies, approved technology platforms, cybersecurity requirements, data governance policies, and operational support models. • Create architecture acceptance criteria and validate that proposed solutions meet functional, technical, security, operational, and business requirements before production deployment. Generative AI and Model Architecture • Design production-grade generative AI solutions using commercial, open-source, hosted, dedicated, and privately deployed models. • Evaluate and select language, vision, speech, embedding, reranking, and multimodal models based on solution quality, latency, cost, context requirements, data sensitivity, deployment options, scalability, supportability, and vendor risk. • Design multi-model architectures that support model routing, fallback, portability, workload specialization, and reduced dependency on a single model provider. • Define prompt architecture, context assembly, structured-output requirements, response validation, model fallback, caching, rate limiting, and error-handling patterns. • Evaluate model performance using repeatable technical and business criteria rather than vendor benchmarks or demonstration results alone. • Maintain awareness of model capabilities, limitations, licensing considerations, deployment constraints, and rapidly changing AI platform capabilities. Agentic AI Architecture • Design secure and reliable AI agents that can reason, use tools, maintain state, interact with enterprise applications, and execute controlled business workflows. • Define agent responsibilities, tool boundaries, memory models, workflow states, delegation rules, approval requirements, and termination conditions. • Design single-agent and multi-agent solutions using deterministic workflow controls around nondeterministic model behavior. • Establish architecture patterns for human-in-the-loop review, escalation, exception handling, retries, timeouts, circuit breakers, compensating actions, and emergency termination. • Define secure agent-to-user, agent-to-agent, and agent-to-tool interaction patterns. • Prevent uncontrolled agent autonomy by enforcing least privilege, constrained tool access, transaction limits, validation rules, and approval gates for consequential actions. • Partner with AI engineering teams to establish consistent agent development, orchestration, testing, deployment, and lifecycle management standards. Retrieval-Augmented Generation and Knowledge Architecture • Design enterprise retrieval-augmented generation solutions across structured, unstructured, document, transactional, graph, and operational data sources. • Define document ingestion, parsing, chunking, metadata enrichment, embedding, indexing, retrieval, reranking, citation, and knowledge-refresh strategies. • Design lexical, semantic, vector, hybrid, graph-enhanced, and structured retrieval patterns based on the characteristics of each use case. • Ensure retrieval solutions preserve source-system security, authorization, data classification, retention, and user entitlements. • Establish patterns for authorization-aware retrieval, source attribution, content freshness, provenance, and deletion. • Define controls for retrieval poisoning, outdated content, duplicate information, conflicting sources, inappropriate data exposure, and unsupported model responses. • Evaluate retrieval quality, answer relevance, groundedness, citation accuracy, and knowledge coverage before production deployment. AI Evaluation and Quality Engineering • Define measurable quality standards and evaluation strategies for generative AI, retrieval, machine learning, and agentic AI solutions. • Establish golden datasets, benchmark scenarios, regression suites, adversarial tests, and business acceptance criteria. • Define evaluation methods for accuracy, relevance, groundedness, hallucination, toxicity, bias, safety, retrieval quality, tool selection, tool-call accuracy, agent trajectory, and task completion. • Implement automated evaluation gates within AI development and deployment pipelines. • Define the appropriate use of human evaluation, expert review, LLM-based evaluation, deterministic testing, and statistical analysis. • Ensure model, prompt, retrieval, agent, and tool changes are tested against previous production behavior before release. • Establish production quality thresholds, monitoring requirements, rollback criteria, and exception-management processes. AI Security, Identity, and Trust Architecture • Design AI solutions in accordance with enterprise cybersecurity, privacy, identity, compliance, and risk-management requirements. • Perform AI-specific threat modeling covering prompt injection, indirect prompt injection, data poisoning, retrieval poisoning, sensitive-data exposure, model extraction, system-prompt leakage, insecure tool invocation, excessive agency, and downstream code execution. • Define identity propagation and authorization patterns across users, agents, models, tools, APIs, applications, data sources, and external services. • Design least-privilege access, workload identities, delegated authorization, service accounts, session isolation, tenant isolation, and approval controls. • Ensure agents cannot access data, tools, or transactions beyond the permissions of the requesting user or approved system identity. • Define security controls for AI gateways, model endpoints, vector stores, knowledge bases, MCP servers, external tools, plugins, third-party models, and vendor services. • Establish auditability that records who initiated an AI request, what context was used, which decisions were made, which tools were invoked, who approved an action, and what action was executed. . click apply for full job details

Job Description Top Skills: 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 previous experience as an Architect implementing A.I into an Enterprise Environment Must have experience with Enterprise AI Platforms Enterprise Standards and Patterns Enterprise AI Strategy Target-State Architecture Architecture Governance Executive Influence Description 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