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Manager Platform Development

North Chicago,IL

1051 Manager Platform Development jobs in North Chicago,IL

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Senior Research & Development Manager

Kraft/Heinz

Arlington Heights, IL 60004

$151,000 bonus
Hybrid~ 39 minFlexible ScheduleHealth InsurancePaid Time OffRetirement Benefit

  • BS/MS/PhD degree in Engineering, Materials, Chemical or Packaging Engineering preferred or related discipline.
  • Minimum 8 years of R&D; technology development and commercialization experience in packaging and project leadership experience (consumer goods, food & beverage, pharma or similar preferred).
  • Demonstrated experience managing end-to-end development projects, identifying and driving packaging technology initiatives, leading external partnerships, and coordinating cross-functional teams.
  • Demonstrated experience in people management
  • Strong technical knowledge of" materials" (polymers, barriers and additives , etc.) and" manufacturing constraints" , including converting processes (e.g., filling, labeling, molding , injection molding, extrusion blow molding, thermoforming ), bottle closure and dispensing systems .
  • Proven project management/PMO skills; experience with governance, risk management, budget management, and stakeholder reporting.
  • Expert knowledge of design of experiments and statistics; strong technical problem solving.
  • Excellent communication, negotiation and interpersonal skills; ability to influence at all levels.
  • Previous experience leading complex, multisite or global packaging or technology launches.
  • Expert knowledge of sustainable packaging solutions, material/package industry standards and regulations.
  • Experience with pilot line setups and scale-up to industrial production.
  • Willingness and ability to travel up to 25% of the time .
  • Strong technical knowledge of" materials" (polymers, barriers and additives , etc.) and" manufacturing constraints" , including converting processes (e.g., filling, labeling, molding , injection molding, extrusion blow molding ), bottle closure and dispensing systems .
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Area Manufacturing Manager - Controls

Packaging Corporation Of America

Lake Forest, IL 60045

$185,000 bonus
~ 16 min OnsiteUrgently HiringHealth InsurancePaid Time OffRetirement Benefit

  • Bachelor's degree in Electrical Engineering or related field, or equivalent military experience/training
  • 10+ years in Controls, Manufacturing, or Field Technical Support
  • 5+ years of papermill experience required
  • Expertise in VFDs (AC, DC, Servo), preferably Rockwell
  • Strong knowledge of converting machine controls and PLC projects
  • Ability to travel up to 80% within the U.S.; must reside in the lower 48 states
  • Excellent communication, organizational, and planning skills
  • Proficiency in Microsoft Office (Excel, Outlook, PowerPoint, Word)
  • Strong knowledge of NEC, NFPA-70E, and electrical safety standards
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New, Posted 1 day ago
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Senior Research & Development Manager

Kraft/Heinz

Skokie, IL 60076

$151,000 bonus
Hybrid~ 44 minFlexible ScheduleHealth InsurancePaid Time OffRetirement Benefit

  • BS/MS/PhD degree in Engineering, Materials, Chemical or Packaging Engineering preferred or related discipline.
  • Minimum 8 years of R&D; technology development and commercialization experience in packaging and project leadership experience (consumer goods, food & beverage, pharma or similar preferred).
  • Demonstrated experience managing end-to-end development projects, identifying and driving packaging technology initiatives, leading external partnerships, and coordinating cross-functional teams.
  • Demonstrated experience in people management
  • Strong technical knowledge of" materials" (polymers, barriers and additives , etc.) and" manufacturing constraints" , including converting processes (e.g., filling, labeling, molding , injection molding, extrusion blow molding, thermoforming ), bottle closure and dispensing systems .
  • Proven project management/PMO skills; experience with governance, risk management, budget management, and stakeholder reporting.
  • Expert knowledge of design of experiments and statistics; strong technical problem solving.
  • Excellent communication, negotiation and interpersonal skills; ability to influence at all levels.
  • Previous experience leading complex, multisite or global packaging or technology launches.
  • Expert knowledge of sustainable packaging solutions, material/package industry standards and regulations.
  • Experience with pilot line setups and scale-up to industrial production.
  • Willingness and ability to travel up to 25% of the time .
  • Strong technical knowledge of" materials" (polymers, barriers and additives , etc.) and" manufacturing constraints" , including converting processes (e.g., filling, labeling, molding , injection molding, extrusion blow molding ), bottle closure and dispensing systems .
SmartExplore AI is experimental.
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New, Posted 18 hours ago
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Operations Leadership Development Program

Legrand

Kenosha, WI 53143

New, Posted 1 hour ago
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Enterprise Architect

TEKsystems

Lincolnshire, IL 60069

$90-$110/hr
~ 26 min OnsiteHealth InsurancePaid Time OffRetirement Benefit

  • 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.
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New, Posted 1 day ago
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Corporate Quality Assurance / Systems Manager

Scot Forge

Spring Grove, IL 60081

~ 45 min OnsiteEducation AssistanceHealth InsurancePaid Time OffRetirement Benefit

  • Bachelor of Science degree in Engineering or Metallurgy (or equivalent).
  • Minimum of 7 years quality assurance leadership experience in a manufacturing environment.
  • Six Sigma Black Belt with multiple projects completed.
  • Qualified Lead Auditor in quality systems such as ISO 9001:2008 or AS9100.
  • This position is primarily located in our Spring Grove, IL facility with occasional travel to plants in other locations as required (Franklin Park, IL and Clinton, WI).
  • Scot Forge requires all employees to work 100% onsite.
  • We maintain a drug-free workplace and perform pre-employment substance abuse testing.
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New, Posted 1 day ago
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Manager, Healthcare AI Revenue Cycle Management

KPMG

Chicago, IL 60629

~ 51 min OnsiteHealth InsuranceRetirement Benefit

  • Minimum five years of consulting experience focused on operations performance improvement, systems integration, or equivalent industry experience (front, middle and back office experience preferred)
  • Minimum two years of direct supervisory experience, with preference of two to three years of experience managing teams
  • Bachelor's degree from an accredited college/university
  • Progressive experience using AI tools in implementing operational improvement plan across all phases of healthcare revenue cycle management and patient access including financial clearance, scheduling, referral management, billing, follow-up/collections and vendor management
  • Experience building AI tools to handle, analyze and evaluate large data sets with advanced analytics and visualization tools; working with project management teams, drafting status reports, tracing issues, monitoring resources, and workflow analysis/design
  • Deep understanding of the intersection of AI capabilities around scheduling/registration functions, financial clearance, clinical documentation, care delivery and care coordination, patient flow/throughput, referral management, clinical/operational/financial analytics, reporting and key initiatives such as patient experience transformations
  • Proficiency with foundational consulting skills such as analytical, written and verbal communication; possess appropriate client presence, facilitation and presentation abilities
  • Must be authorized to work in the U.S. without the need for employment-based visa sponsorship now or in the future. KPMG LLP will not sponsor applicants for U.S. work visa status for this opportunity (no sponsorship is available for H-1B, L-1, TN, O-1, E-3, H-1B1, F-1, J-1, OPT, CPT or any other employment-based visa)
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Client Service Manager - Multinational Inbound Small Business

Gallagher

Rolling Meadows, IL 60008

~ 45 min Onsite

  • Bachelor's degree with 1+ years of client service and/or claims management experience, OR
  • High School diploma/GED with 3+ years of client service and/or claims management experience.
  • Licensing: Property and Casualty Insurance License.
  • Technical Skills: Proficiency in Microsoft Office and familiarity with Applied Epic systems.
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Remote Benefits Specialist

Globe Life Virtual Sales

North Chicago, IL

RemoteFlexible Schedule

  • No Experience Required
  • No prior experience is required
  • Reliable internet and a quiet workspace
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New, Posted 11 hours ago
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Assistant Manager (Retail Operations)

ALDI

Niles, IL 60714

$26/hr
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