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Scale Ai Data Labeling Jobs (Flexible Options) in Iowa

Bespoke Labs

Data Engineer

Iowa City, IA · On-site

$99K - $119K/yr

About Us We are AI researchers and builders who understand how to curate data and RL environments ... scale RL training possible. This is a high-ownership role; you will be building novel systems, not ...

Bespoke Labs

Data Engineer

Des Moines, IA · On-site

$111K - $134K/yr

About Us We are AI researchers and builders who understand how to curate data and RL environments ... scale RL training possible. This is a high-ownership role; you will be building novel systems, not ...

Bespoke Labs

Data Engineer

Davenport, IA · On-site

$108K - $130K/yr

About Us We are AI researchers and builders who understand how to curate data and RL environments ... scale RL training possible. This is a high-ownership role; you will be building novel systems, not ...

Bespoke Labs

Data Engineer

Clinton, IA · On-site

$108K - $130K/yr

About Us We are AI researchers and builders who understand how to curate data and RL environments ... scale RL training possible. This is a high-ownership role; you will be building novel systems, not ...

Bespoke Labs

Data Engineer

Sioux City, IA · On-site

$113K - $136K/yr

About Us We are AI researchers and builders who understand how to curate data and RL environments ... scale RL training possible. This is a high-ownership role; you will be building novel systems, not ...

Bespoke Labs

Data Engineer

Waterloo, IA · On-site

$106K - $128K/yr

About Us We are AI researchers and builders who understand how to curate data and RL environments ... scale RL training possible. This is a high-ownership role; you will be building novel systems, not ...

Bespoke Labs

Data Engineer

Cedar Rapids, IA · On-site

$112K - $135K/yr

About Us We are AI researchers and builders who understand how to curate data and RL environments ... scale RL training possible. This is a high-ownership role; you will be building novel systems, not ...

Jobtailor

Director, Data Management

Des Moines, IA · On-site

$170 - $250/hr

Collaborate with analytics and data science teams to enable advanced analytics, AI/ML, and self ... Proven success leading large-scale data modernization programs in Azure or similar cloud ecosystems ...

New

Franzenburg

AI Adoption/ Implementation Manager

Des Moines, IA · On-site

  • Medical

  • Dental

  • Vision

  • Retirement

  • PTO

... scale operations--particularly around customer relationship management and business growth. Key ... Ensure secure, compliant, and reliable AI usage (data privacy, hallucination mitigation, output ...

Accenture

Gemini Enterprise for CX Sr. Engineer

Des Moines, IA · On-site

$94K - $266K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

We Are Accenture is a premier Google Cloud partner helping organizations modernize data ecosystems, build real-time analytics capabilities, and responsibly scale AI. As part of Accenture Cloud First ...

New

Kellton

Imaging Data Scientist

Johnston, IA · On-site

$75/hr

... biology and applied AI to build, productionize, and maintain computer vision pipelines that ... Operationalize at scale: batch processing of tens of thousands of structures/images; optimize ...

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AI & Automation Solution Architect

Jobtailor

Des Moines, IA • On-site

$120 - $170/hr

Other

This job post has expired today. Applications are no longer accepted.

Job description

Responsibilities
  • Design AI and Automation Solution Patterns
    • Develop reusable AI and automation solution patterns that translate business use cases into practical conceptual, logical, and technical designs.
    • Define repeatable design approaches, documentation standards, testing expectations, support considerations, and measurement practices that can be adopted across business and technology teams.
    • Partner with enterprise architecture to ensure solution patterns align with enterprise standards, integration principles, security expectations, and cloud architecture direction.
  • Build Reference Implementations and Proofs of Value
    • Build or co-build prototypes, proof-of-value implementations, reusable components, and demonstration scenarios that validate AI and automation approaches.
    • Evaluate emerging tools, platforms, integration patterns, automation capabilities, and technical assumptions through hands‑on reference implementations.
    • Document implementation lessons, constraints, and recommended patterns so solutions can be replicated, adapted, extended, and moved toward production.
  • Provide Technical Advisory and Design Support
    • Serve as a technical advisor to business, product, process improvement, and technology delivery teams pursuing AI and automation opportunities.
    • Translate use cases, workflow impacts, business requirements, and adoption needs into practical solution options in partnership with the AI & Automation Enablement Lead.
    • Guide teams through tool selection, architecture, integration, data, security, controls, testing, deployment, scaling considerations, and technical risk tradeoffs.
  • Support Responsible AI and Automation Practices
    • Embed responsible AI and automation practices into solution design, including transparency, human oversight, data protection, security, privacy, explainability where appropriate, and appropriate use limitations.
    • Partner with governance, risk, compliance, legal, privacy, information security, and data governance teams to ensure solutions follow enterprise guardrails.
    • Identify, document, and support mitigation of AI‑specific risks, production readiness needs, human‑in‑the‑loop controls, operational monitoring, and auditability requirements.
  • Define Technical Standards, Templates, and Reusable Assets
    • Create and maintain technical templates, reference architectures, design checklists, prompt engineering patterns, automation standards, testing guides, and production readiness materials.
    • Develop reusable components, scripts, connectors, workflow patterns, prompt libraries, configuration examples, and technical accelerators where appropriate.
    • Partner with CoE leadership to ensure technical assets are understandable, reusable, aligned with business‑facing playbooks, and continuously improved based on implementation lessons.
  • Support Lifecycle Execution from Intake to Scale
    • Support the end‑to‑end AI and automation lifecycle, from opportunity assessment and solution design through prototype development, governance alignment, testing, deployment readiness, adoption, measurement, and scale.
    • Define production readiness expectations for technical design, documentation, ownership, monitoring, support model, controls, adoption needs, and benefit tracking.
    • Partner with delivery teams to plan pilot‑to‑production transitions, resolve technical barriers, and ensure solutions are secure, supportable, observable, maintainable, and aligned with enterprise architecture expectations.
  • Enable Distributed Delivery Teams
    • Coach technology and business teams on approved AI and automation patterns, tools, standards, delivery practices, and responsible use of CoE‑provided guidance and reusable assets.
    • Provide technical enablement through demos, design walkthroughs, knowledge‑sharing sessions, office hours, and communities of practice.
    • Help teams determine when to use generative AI, workflow automation, RPA, low‑code/no‑code tools, APIs, data services, or traditional application capabilities while promoting reuse over one‑off solutions.
  • Partner Across Platforms, Data, Architecture, and Security
    • Collaborate with enterprise architecture, cloud/platform teams, data and analytics, application teams, security, identity/access management, infrastructure, and operations.
    • Ensure solutions consider data readiness, integration needs, access controls, platform constraints, system performance, monitoring, operational support, scalability, and vendor/platform limitations.
    • Support alignment with approved platforms, enterprise technical standards, capability roadmaps, and technical enablement needs.
Qualifications
  • 5+ years of experience in solution architecture, application development, automation engineering, systems integration, enterprise applications, digital transformation, or a related technology field required.
  • Experience designing and delivering AI, automation, workflow, low-code/no-code, data-enabled, or digital business solutions using enterprise applications, APIs, integrations, and data services required.
  • Experience with Microsoft cloud, AI, automation, and low-code/no-code platforms; responsible AI, AI governance, data privacy, information security, model risk, vendor risk, regulatory, or compliance considerations; and regulated industry environments such as insurance, financial services, or healthcare strongly preferred.
  • Familiarity with advanced AI and automation practices, including MLOps, LLMOps, prompt engineering, retrieval-augmented generation, document intelligence, process mining, workflow orchestration, knowledge management, or API-based integrations preferred.
  • Experience developing prototypes, proofs of value, reusable technical assets, reference architectures, enterprise solution patterns, or supporting design reviews, architecture governance, agile delivery, product management, design thinking, Lean, Six Sigma, DevOps, continuous improvement, communities of practice, or technical training preferred.
  • Strong knowledge of solution design, systems integration, security-by-design, testing, documentation, production readiness, supportability, operational handoff, and translating business needs into practical solution options.
  • Strong collaboration, problem-solving, facilitation, consulting, documentation, stakeholder management, and communication skills, with experience working across business, product, architecture, security, data, compliance, risk, and technology teams.
  • Knowledge of solution architecture, systems integration, AI and automation technologies, responsible AI practices, governance, security, production readiness, and operational support considerations.
  • Translate business needs into practical AI, automation, workflow, and digital solution designs aligned with enterprise standards.
  • Develop reusable solution patterns, prototypes, technical templates, reference architectures, and other enablement assets that support repeatable delivery.
  • Evaluate tools, platforms, integration patterns, and automation capabilities through hands‑on experimentation and documented lessons learned.
  • Advise, coach, and enable business and technology teams on approved AI and automation practices, responsible use, and pilot-to-production execution.
  • Partner across architecture, data, security, compliance, risk, governance, operations, and delivery teams to ensure solutions are secure, scalable, supportable, and aligned with enterprise expectations.
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