556 Architecture Engineering jobs in Waukegan,IL

Hybrid~ 22 minHealth InsurancePaid Time OffRetirement Benefit

Senior Staff Engineer

Packaging Corporation Of America

Lake Forest, IL 60045

$170,000 bonus

Bachelor's degree in mechanical engineering or related field (or equivalent experience)
10+ years in industrial paper, corrugating, or design engineering environments
Ability to travel extensively (80%) – minimum 3 weeks per month

Mechanics and Engineering

United States Army

Benet Lake, WI 53102

Senior Design Assurance Quality Engineer I-II

Disability Solutions

Libertyville, IL 60048

$90,000-$125,000/yr

New, Posted just now

~ 32 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.

Hybrid~ 50 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.

Mechanics and Engineering

United States Army

Woodworth, WI 53194

New, Posted 1 day ago

Operations Leadership Development Program

Legrand

Kenosha, WI 53141

New, Posted 7 hours ago

HVAC Controls Systems Engineer

Johnson Controls

Arlington Heights, IL 60005

~ 45 min Onsite

Advanced degree in a relevant field, or a university degree with an equivalent combination of education and experience.
6+ years of progressive experience in system design and integration, ideally in complex controls or related technical environments.
Demonstrated mastery of design platforms and the ability to apply AutoCAD and BIM in advanced engineering work.
Strong background in hardware design oversight and software architecture for integrated control solutions.
Proven strategic thinking and problem-solving skills with the ability to guide decisions in complex situations.
Experience communicating technical concepts clearly to engineering, sales, project, and customer stakeholders.
Ability to mentor senior engineers and influence engineering practices across a team.
Strong attention to quality, documentation, and technical consistency.

iHire

Food Expeditor

The Carrington at Lincolnwood

Lincolnwood, IL 60712

$21-$23/hr

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