940 Digital Solution Architect jobs in Lombard,IL

~ 51 min OnsiteHealth InsurancePaid Time OffRetirement Benefit

Enterprise Architect

TEKsystems

Lincolnshire, IL 60069

$90-$110/hr

Enterprise Architect
Hands-on technology leader
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.
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.
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.
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.
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.
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, transaction boundaries, data contracts, API contracts, semantic contracts, and integration responsibilities.
Ensure AI solutions appropriately address retries, timeouts, idempotency, circuit breakers, error handling, compensating transactions, and recovery.
Define patterns for human-in-the-loop workflows, approvals, exception handling, and escalation.
Maintain the enterprise AI integration map, documenting dependencies and touchpoints among AI capabilities and enterprise platforms.
Identify reusable enterprise services and integrations that can accelerate multiple AI initiatives.
Establish enterprise standards for evaluating AI models, agents, retrieval systems, prompts, workflows, and business outcomes.
Define offline and online evaluation approaches appropriate to each AI use case.
Establish task-specific quality measures covering accuracy, groundedness, relevance, completeness, factuality, safety, latency, reliability, and cost.
Define evaluation methods for retrieval quality, agent trajectories, tool selection, tool execution, workflow completion, and human intervention.
Establish regression testing for prompts, models, retrieval configurations, agents, workflows, integrations, and platform changes.
Define adversarial testing, red-team testing, edge-case testing, and failure-mode testing requirements.
Establish human evaluation, adjudication, feedback, and quality review processes where automated evaluation is insufficient.
Define quality gates for development, testing, release, production rollout, and model or configuration changes.
Ensure that AI quality is measured continuously and not determined solely through demonstrations, subjective user feedback, or initial pilot results.
Define the enterprise operating model for AI solution development, testing, deployment, monitoring, support, and retirement.
Establish standards for CI/CD, infrastructure as code, configuration management, prompt management, model versioning, agent versioning, and environment promotion.
Define release strategies, including feature flags, canary deployments, controlled rollout, rollback, and fallback.
Establish observability requirements across models, agents, prompts, retrieval, tools, APIs, workflows, infrastructure, and business outcomes.
Define standards for distributed tracing, token usage, latency, errors, tool calls, retrieval results, quality, safety events, and consumption costs.
Establish controls for quotas, rate limits, capacity, concurrency, resource utilization, and unbounded consumption.
Define operational responsibilities, service-level expectations, support models, incident response, escalation, recovery, and post-incident review.
Establish FinOps practices for model inference, AI platform consumption, data movement, storage, and supporting infrastructure.
Ensure AI capabilities can be operated reliably by enterprise support and engineering teams after initial delivery.
Define security architecture and threat-modeling requirements for AI models, agents, platforms, applications, data, tools, integrations, and workflows.
Establish controls for direct and indirect prompt injection, sensitive information disclosure, insecure outputs, model manipulation, data poisoning, and supply-chain risk.
Define controls to prevent excessive agency, overprivileged access, unauthorized actions, and uncontrolled execution.
Establish least-privilege access patterns for models, agents, tools, APIs, data, and enterprise systems.
Define requirements for agent identity, workload identity, secrets management, credential handling, sandboxing, isolation, and egress control.
Establish human authorization requirements for consequential, irreversible, financial, customer-facing, security-sensitive, or legally significant actions.
Define monitoring and response requirements for misuse, anomalous behavior, model extraction, data leakage, abuse, and unexpected consumption.
Partner with cybersecurity, privacy, legal, compliance, and risk teams to ensure AI controls are technically implementable and operationally effective.
Ensure that security requirements are built into architecture and delivery rather than added after implementation.
Translate AI policies, principles, legal requirements, and risk expectations into specific architecture and engineering controls.
Define risk-based architecture requirements based on use-case impact, data sensitivity, autonomy, audience, and potential consequences.
Establish architecture checkpoints, approval requirements, exception processes, and escalation paths without unnecessarily slowing delivery.
Define requirements for transparency, explainability, disclosure, human oversight, traceability, audit evidence, and accountability.
Maintain or contribute to the enterprise inventory of AI use cases, models, agents, platforms, vendors, risks, and accountable owners.
Establish lifecycle requirements covering experimentation, approval, production use, monitoring, material changes, suspension, and retirement.
Ensure vendor AI capabilities are subject to appropriate architecture, security, privacy, operational, and risk evaluation.
Balance innovation and speed with proportionate controls based on actual enterprise risk.
Define and maintain AI architecture principles, standards, patterns, decision trees, guardrails, reference architectures, and technology decision records.
Lead or support Architecture Review Board reviews for AI-related initiatives and architecture-significant changes.
Ensure that architecture governance produces timely decisions and practical delivery guidance rather than unnecessary process.
Develop target-state, current-state, transition, capability, information, application, integration, security, and technology architecture views.
Identify architecture dependencies, constraints, technical debt, transition risks, and sequencing requirements.
Establish reusable architecture artifacts that engineering and product teams can directly apply.
Maintain traceability from business objectives and requirements to architecture decisions and delivered capabilities.
Provide architecture exceptions when justified, with explicit risks, conditions, expiration dates, and remediation plans.
Continuously improve AI architecture governance based on delivery results, production incidents, technology changes, and lessons learned.
Expert Level
Contract position based out of Lincolnshire, IL.
Pay range for this position is $90.00 - $110.00/hr.
This position is fully onsite in Lincolnshire, IL.

DiversityJobs

Marketing Manager

Energy Solutions

Chicago, IL 60603

$85,000-$115,000/yr

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Solar Programs, Associate - Consumer Protection (Chicago, IL - US)

Energy Solutions

Chicago, IL 60603

$60,000-$70,000/yr

Bilingual Preferred

New, Posted 1 day ago

Development Manager

NorthPoint Development LLC

Downers Grove, IL 60515

~ 32 min OnsiteChildcare AssistanceEducation AssistanceHealth InsurancePaid Time OffRetirement Benefit

Computational Designer

USA Thornton Tomasetti

Chicago, IL 60629

$70,000-$85,000/yr

Bachelor's degree in Architecture, Engineering, Computational Design, Digital Design, or a related field
0-3 years of experience in the AEC or digital design industry
Advanced proficiency in Rhino and Grasshopper with experience building parametric workflows
Experience contributing to VDC, computational design, or digital modeling initiatives
Strong visual communication and graphic design skills, with an emphasis on clarity and storytelling
Experience producing animations, renderings, or digital visualizations
Strong collaboration, organization, and communication skills
A portfolio showcasing relevant computational design, visualization, graphic communication, animation, or related digital work is required with the application submission.

~ 32 min OnsiteHealth InsuranceRetirement Benefit

Manager, Boomi Integration Architect

KPMG

Chicago, IL 60629

Minimum of five years of experience in enterprise software engineering, with at least 3+ years in a senior integration developer role.
Bachelor's degree from an accredited college or university is required
Certifications in major integration platforms (e.g., MuleSoft Certified Integration Architect, Boomi Enterprise Architect) is preferred
Experience in a client-facing consulting role
Excellent communication, stakeholder management, and presentation skills
Deep understanding of integration design patterns, Event-Driven Architecture (EDA), and microservices
Expert knowledge of REST, SOAP, GraphQL, JSON, XML, EDI, and SQL
Strong grasp of modern authentication and security protocols (OAuth 2.0, OIDC, JWT, SAML)
Extensive hands-on experience with leading enterprise integration platforms (e.g., MuleSoft, Boomi, Informatica, SnapLogic, Workato, or similar iPaaS/ESB solutions)
Strong working knowledge of major cloud platforms (AWS, Azure, or GCP) and related cloud-native integration services (e.g., AWS SQS/SNS, Azure Service Bus, Pub/Sub)
Experience with CI/CD pipelines and DevOps practices in an integration context
Ability to travel as needed
Applicants must be authorized to work in the U.S. without the need for employment-based visa sponsorship now or in the future

~ 32 min OnsiteHealth InsurancePaid Time OffRetirement Benefit

Arch Spec CAD Designer Job

Armstrong World Industries

Chicago, IL 60629

$68,000-$74,000/yr

Associate or bachelor's degree in architecture, industrial design, or a related field
Minimum 1-3 years' (Level I) experience in architecture and/or product design
Minimum 3-5 years' (Levels II) experience in architecture and/or product design
Knowledge of Windows Applications, including Microsoft Office and operating systems
Comfortable in high tolerance CAD/CAM production via CNC, as well as the production of shop drawings
Knowledge of digital fabrication techniques as well as CNC and/or prototyping experience is necessary
Ability to read and translate construction documents, ideation and form development, knowledge of materials and manufacturing process, firsthand experience in mock-ups, prototyping & mechanical aptitude
Proficient in Rhino 3D and Adobe CC (Photoshop, InDesign, Illustrator) required
Experience with Grasshopper/Dynamo, Revit/AutoCAD preferred
Visualization skills in vRay, 3DS Max, etc. a plus
Level II preferred MA
Captioned education: MA preferred for Levels II but not required

New, Posted 1 day ago

OnsiteFlexible ScheduleHealth InsurancePaid Time OffRetirement Benefit

Associate Director, Category Strategy & Insights Taste Elevation

Kraft/Heinz

Forest View, IL 60402

$205,200 bonus

Bachelor's degree in Business, Marketing, Economics, Analytics, Statistics, Consumer Behavior, or a related field.
8+ years of experience in category strategy, category management, category leadership, retail analytics, shopper insights, sales strategy, or related commercial functions.
3+ years of experience leading and developing high-performing teams.
Demonstrated ability to synthesize multiple data sources into actionable business recommendations and category roadmaps.
Strong understanding of retail, omnichannel commerce, category management principles, retailer strategies, and shopper behavior.
Experience working with syndicated data, retailer data, consumer insights, space analytics, and category performance metrics.
Proven success influencing senior leaders, cross-functional stakeholders, and external customer partners.
Exceptional communication, storytelling, presentation, and relationship-building skills.

Hybrid~ 51 minEducation AssistanceHealth InsurancePaid Time OffRetirement Benefit

Enterprise Security Architect

Endeavor Health

Skokie, IL 60076

Bachelor's Degree, or equivalent, in a technical, engineering, or investigative academic discipline, or an equivalent work history and educational background supported by expert-level security certifications relevant to the role.
Two or more expert-level security platform or capability certifications
Nine (9) years in a dedicated, advanced or expert-level IT security engineering role with demonstrated, consistent performance leading security initiatives and developing use cases.
Minimum of four (4) years in security architect roles
Minimum of two (2) years in security project delivery roles, including experience with contracting, architecture, design, and implementation.
Previous experience developing and contributing to a comprehensive, enterprise cybersecurity strategy.
Demonstrated experience instructing, mentoring, or developing junior team members.
A valid driver's license is required if the incumbent is selected to perform related duties at an off-site location. If the incumbent uses his or her personal vehicle, the incumbent must maintain automobile liability coverage as required by law and evidence of such coverage may be requested.

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~ 20 min OnsiteEducation AssistanceHealth InsurancePaid Time OffRetirement Benefit

Buyer

Element Solutions

Itasca, IL 60143

$81,470-$122,206/yr

Bachelor's degree in a business-related field.
5-10 years' experience in procurement, preferred in a manufacturing environment.
Must be able to effectively communicate (read/write/speak) in English.
Experience with an ERP system, JD Edwards is a plus.
Knowledge of Kan-Ban/lean inventory principles a plus.
Strong analytical and financial acumen (cost modeling, PPV, TCO).
Good negotiating skills.
Experience working for a multinational corporation preferred.
Working knowledge of import/export regulations, including dangerous goods is a plus.