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Ai Infrastructure Jobs in Kansas (NOW HIRING)

Sr. Systems Engineer - AI

Kansas City, KS · On-site

$100K - $137K/yr

Collaborate with solution delivery, infrastructure, and application teams to ensure services are fully operationalized, properly monitored, and supportable through standard IT processes * Ensure AI ...

Red Team - Sr Security Engineer

Overland Park, KS · On-site

$113K - $155K/yr

Understanding of how to secure AI infrastructure, including model endpoints, MCP servers and tool integrations, RAG data stores, and API key management for AI services * Experience evaluating third ...

Understanding of how to secure AI infrastructure, including model endpoints, MCP servers and tool integrations, RAG data stores, and API key management for AI services * Experience evaluating third ...

Akuity's customer growth is driven by enterprise companies that need governance and compliance at scale, and by AI-native companies, including AI infrastructure and cloud companies, that rely on ...

We believe AI should amplify human potential, not replace it, and we build with that conviction in ... You're building real infrastructure, not prototypes. That means thinking through orchestration ...

Lead Solution Architect

Wichita, KS · On-site

$150 - $190/hr

Experience designing AI infrastructure on AWS or other cloud platforms. * Experience developing AI governance, security, and operating patterns. * Experience building semantic, metadata, or ...

Through industry-leading Linux, Kubernetes, Edge and AI infrastructure solutions, SUSE delivers the flexibility to innovate everywhere-from the data center to multi-cloud and out to the edge. Only ...

Senior AI Engineer

Overland Park, KS · On-site

$101K - $139K/yr

Prototype, build, and deploy real-time AI applications and infrastructure for speech, voice agents, LLM-powered workflows, and other latency-sensitive AI experiences. * Lead the development of AI ...

New

Sr. Data Engineer - AI

Kansas City, KS · On-site

$110K - $132K/yr

Own key AI data infrastructure components and ensure they are scalable, maintainable, and supported by effective monitoring, logging, documentation, data lineage, and quality metrics * Apply ...

Sr. Data Engineer - AI

Kansas City, KS · On-site

$110K - $132K/yr

Own key AI data infrastructure components and ensure they are scalable, maintainable, and supported by effective monitoring, logging, documentation, data lineage, and quality metrics * Apply ...

Showing results 21-40

Ai Infrastructure information

See Kansas salary details

$25

$52

$77

How much do ai infrastructure jobs pay per hour?

As of Sep 3, 2026, the average hourly pay for ai infrastructure in Kansas is $52.78, according to ZipRecruiter salary data. Most workers in this role earn between $42.88 and $61.54 per hour, depending on experience, location, and employer.

What is AI infrastructure?

AI infrastructure refers to the combination of hardware, software, and cloud-based solutions that support the development, deployment, and scaling of artificial intelligence applications. It includes components such as GPUs, CPUs, storage systems, networking, data management tools, and machine learning frameworks. The goal of AI infrastructure is to provide the computational power and resources needed to train, test, and run AI models efficiently, whether on-premises or in the cloud. Organizations invest in robust AI infrastructure to accelerate innovation, manage large datasets, and ensure the reliability of their AI systems.

What are the key skills and qualifications needed to thrive in AI infrastructure?

To thrive in AI Infrastructure, you need expertise in software engineering, distributed systems, cloud platforms, and a solid understanding of machine learning workflows, often supported by degrees in computer science or related fields. Familiarity with tools like Kubernetes, Docker, Terraform, and cloud services (AWS, GCP, Azure), as well as experience with CI/CD pipelines and monitoring systems, is essential. Strong problem-solving abilities, effective communication, and adaptability help professionals excel in cross-functional teams and rapidly evolving environments. These skills and qualities are crucial for building scalable, reliable systems that power AI applications and support organizational innovation.

What are common challenges faced by professionals working in AI infrastructure roles, and how can they be addressed?

Professionals in AI Infrastructure roles often encounter challenges related to scalability, system reliability, and integration with existing IT environments. Managing rapidly growing datasets and ensuring seamless deployment of machine learning models can be complex, requiring robust automation and monitoring tools. Collaboration with data scientists, software engineers, and DevOps teams is critical to ensure infrastructure meets the evolving needs of AI projects. Staying updated with the latest cloud technologies and best practices can help address these challenges and drive successful AI implementations.

What is the difference between Ai Infrastructure vs Data Engineer?

AspectAi InfrastructureData Engineer
Required CredentialsBachelor's in CS, Engineering, or related; knowledge of cloud platforms and AI toolsBachelor's in CS, Data Science, or related; programming and database skills
Work EnvironmentCloud environments, AI model deployment, infrastructure setupData pipelines, database management, data processing
Employer & Industry UsageTech companies, AI startups, cloud providersTech firms, finance, healthcare, e-commerce

Ai Infrastructure professionals focus on building and maintaining the hardware and software systems that support AI models, while Data Engineers develop and manage data pipelines and databases. Both roles require technical skills and often collaborate but serve different core functions within AI and data ecosystems.

What are AI infrastructure jobs?

AI infrastructure jobs involve designing, building, and maintaining the hardware, software, and network systems necessary to support artificial intelligence applications. These roles often require knowledge of cloud computing, data centers, machine learning frameworks, and system optimization to ensure reliable and efficient AI model deployment and operation.

What are popular job titles related to Ai Infrastructure jobs in Kansas?

For Ai Infrastructure jobs in Kansas, the most frequently searched job titles are:

What job categories do people searching Ai Infrastructure jobs in Kansas look for?

The top searched job categories for Ai Infrastructure jobs in Kansas are:

What cities in Kansas are hiring for Ai Infrastructure jobs?

Cities in Kansas with the most Ai Infrastructure job openings:

Infographic showing various Ai Infrastructure job openings in Kansas as of August 2026, with employment types broken down into 76% Full Time, 20% Part Time, 1% Temporary, and 3% Contract. Highlights an 63% Physical, 4% Hybrid, and 33% Remote job distribution, with an average salary of $109,789 per year, or $52.8 per hour.

Sr. Systems Engineer - AI

DAIRY FARMERS OF AMERICA

Kansas City, KS • On-site

$100K - $137K/yr

Full-time

Posted 29 days ago


Dairy Farmers Of America rating

7.2

Company rating: 7.2 out of 10

Based on 169 frontline employees who took The Breakroom Quiz

27th of 65 rated farming


Job description

Serve as a senior-level technical leader within IT Solutions Delivery, responsible for deploying, operating, and continuously improving production AI-enabled platforms and services that support critical business applications.

This role ensures that AI/ML capabilities are delivered into production environments using the same operational rigor, reliability standards, and support models as enterprise IT infrastructure, enabling consistent uptime, performance, and scalability. The engineer partners closely with application teams, platform engineering, and IT operations to ensure AI services are production-ready, supportable, and aligned to enterprise operational standards.

Job Duties and Responsibilities:

  • Deploy AI/ML solutions into enterprise production environments using repeatable, low-risk release processes
  • Build and maintain automated pipelines that support solution delivery across development, testing, and production
  • Ensure all AI services meet enterprise standards for deployment, configuration, and change management
  • Own day-to-day operations of AI-enabled platforms, ensuring availability, reliability, and performance of business-facing services
  • Establish and enforce site reliability engineering (SRE) practices, including high availability and fault tolerance, capacity planning and auto-scaling, and redundancy and failover strategies
  • Continuously optimize platform performance, resource utilization, and cost efficiency
  • Implement and maintain monitoring, logging, and alerting aligned to enterprise ITOM practices
  • Define and track service health, performance, and data quality metrics for AI-enabled services
  • Configure proactive alerting for degradations or anomalies and integrate with enterprise event management platforms
  • Develop and maintain operational dashboards and visibility tools for ongoing service assurance
  • Provide production support for AI-enabled services, including incident triage and resolution, performing root cause analysis (RCA), and implementing corrective and preventative actions
  • Develop and maintain runbooks and support procedures to enable consistent issue resolution
  • Partner with operations and service desk teams to ensure support readiness and knowledge transfer
  • Integrate AI services into enterprise application and infrastructure ecosystems, ensuring compatibility with existing platforms
  • Collaborate with solution delivery, infrastructure, and application teams to ensure services are fully operationalized, properly monitored, and supportable through standard IT processes
  • Ensure AI components behave as first-class enterprise services within the broader application landscape
  • Ensure all AI services are deployed and operated in alignment with enterprise security, compliance, and data protection standards
  • Manage the full operational lifecycle of AI-enabled services, including versioning and controlled releases, performance tuning and optimization, and continuous improvement of deployment and support processes
  • Identify opportunities to automate and standardize platform operations to improve efficiency and reliability
  • Serve as a subject matter expert in AI platform operations
  • Lead or support resolution of major incidents and complex operational challenges
  • Drive adoption of standardized operational practices, including runbooks, and reliability engineering
  • Provide guidance to project teams to ensure solutions are designed for production support from day one

Education and Experience

  • Undergraduate degree in computer science, information technology, or related curriculum (or equivalent combination of experience and education)
  • 8 or more years of information technology, cloud or platform engineering, DevOps, site reliability engineering (SRE), infrastructure engineering, application operations, or related experience that includes experience:
    • supporting AI/ML platforms, machine learning operations (MLOps), AI-enabled applications, large-scale data platforms, or other advanced analytics environments in production
    • deploying, monitoring, and supporting business-critical applications in cloud-based and hybrid enterprise environments
    • designing and managing CI/CD pipelines, automated deployment processes, and infrastructure-as-code solutions
    • partnering with application development, infrastructure, security, and operations teams to operationalize new technologies and services
    • serving as a technical lead, senior engineer, or escalation point for complex production issues
  • Certification and/or License - may be required during course of employment

Knowledge, Skills, and Abilities

  • Deep understanding of managing supported systems in a large-scale environment
  • Solid understanding of AI/ML operational practices, including model deployment, model monitoring, inference services, version management, and AI platform lifecycle management
  • Strong understanding of backup technologies and cloud technologies
  • Strong scripting and automation skills
  • Strong collaboration skills with application development, platform engineering, cybersecurity, infrastructure, and service desk teams
  • Strong problem solving and analytical skills with the ability to quickly isolate problems, collect data, establish facts, and draw valid conclusions; able to perform root cause analysis and implement sustainable corrective and preventive actions
  • Able to deploy and maintain highly available, scalable, and supportable AI-enabled services in production environments
  • Able to automate operational processes, platform provisioning, deployments, monitoring, and recovery activities
  • Able to serve as the senior technical escalation point for critical incidents and complex operational challenges
  • Able to influence teams and drive adoption of enterprise operational standards and best practices
  • Able to communicate complex technical concepts to both technical and non-technical stakeholders
  • Able to prioritize multiple operational demands in fast-paced production environments
  • Able to work independently with limited direction while maintaining accountability for enterprise-critical service
  • Must be able to read, write and speak English

An Equal Opportunity Employer including Disabled/Veterans


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