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Ai Reliability Engineer Jobs in Wisconsin (NOW HIRING)

Springs Window Fashions

Senior AI Engineer

Middleton, WI

$107K - $147K/yr

Position Summary The Sr AI Engineer serves as a technical leader responsible for enterprise-scale ... Optimize model performance, scalability, reliability, and operational efficiency. * Evaluate ...

Springs Window Fashions

Senior AI Engineer

Middleton, WI · On-site

$107K - $147K/yr

Position Summary The Sr AI Engineer serves as a technical leader responsible for enterprise-scale ... Optimize model performance, scalability, reliability, and operational efficiency. * Evaluate ...

Springs Window Fashions

Senior AI Engineer

Middleton, WI

$107K - $147K/yr

Position Summary The Sr AI Engineer serves as a technical leader responsible for enterprise-scale ... Optimize model performance, scalability, reliability, and operational efficiency. * Evaluate ...

Zywave

AgenticOps Engineer

WI · On-site +1

Description The Agentic Ops Engineer operates as a cross-team specialist responsible for the health, reliability, and continuous improvement of AI agent output across the organization. They are not ...

Zywave

AgenticOps Engineer

WI · On-site

The Agentic Ops Engineer operates as a cross-team specialist responsible for the health, reliability, and continuous improvement of AI agent output across the organization. They are not embedded in ...

HirexHire

IFD AI Engineer

Milwaukee, WI · Remote

THE ROLE Our client is seeking an Internal Forward Deployed AI Engineer to build and deploy AI ... Implement monitoring, evaluation, frameworks, and performance tracking to ensure the reliability ...

Wisconsin

AI Innovation Engineer

Madison, WI · On-site +1

$90K - $135K/yr

Testing and validating AI agents to ensure accuracy and reliability. * Assisting in the management ... Advanced programming experience in JavaScript/TypeSript or Python (preferred 2 years) * Proven ...

Imedica

Senior AI Engineer

Madison, WI

$113K - $194K/yr

... performance, reliability and end-to-end safety and transparency. * Drive AI Native Use Cases ... Upload high engineering standards and share best practices in code review and design discussions.

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Ai Reliability Engineer information

What are the key skills and qualifications needed to thrive as an AI Reliability Engineer, and why are they important?

To thrive as an AI Reliability Engineer, you need a solid background in computer science or engineering, expertise in AI/ML concepts, and experience with software testing and reliability methodologies. Familiarity with tools like TensorFlow, PyTorch, CI/CD pipelines, and reliability testing frameworks, along with certifications in cloud platforms (e.g., AWS Certified Machine Learning), is highly valuable. Analytical thinking, problem-solving abilities, and strong collaboration skills set top performers apart in this role. These skills ensure robust, dependable AI systems that meet performance standards and maintain trust in critical applications.

What is the difference between Ai Reliability Engineer vs Data Scientist?

AspectAi Reliability EngineerData Scientist
Required CredentialsBachelor's or master's in CS, engineering, or related; certifications in AI/MLBachelor's or master's in CS, statistics, or related; certifications in data analysis or ML
Work EnvironmentTech companies, AI-focused teams, engineering departmentsResearch labs, tech firms, analytics teams
Employer & Industry UsageAI product development, machine learning systems, reliability testingData analysis, predictive modeling, business insights

While both roles involve AI and ML, Ai Reliability Engineers focus on ensuring AI system robustness and uptime, whereas Data Scientists analyze data to generate insights and models. The roles often collaborate but serve different primary functions within AI projects.

What are AI Reliability Engineers?

AI Reliability Engineers are professionals responsible for ensuring that artificial intelligence systems function reliably, safely, and effectively over time. They work on monitoring AI models in production, identifying and mitigating potential failures, and improving the robustness of AI systems. Their tasks often include testing, validation, performance monitoring, and implementing best practices for maintaining AI infrastructure. By focusing on reliability, they help organizations deploy AI solutions that are dependable and trustworthy in real-world environments.

What are some common challenges Ai Reliability Engineers face when ensuring model robustness in production environments?

Ai Reliability Engineers often encounter challenges such as monitoring AI model performance for drift or unexpected behavior, managing data quality issues, and implementing automated alerting systems for anomalies. In production, it's crucial to ensure that AI models operate consistently and remain reliable under varying conditions and data inputs. Collaborating closely with data scientists, software engineers, and DevOps teams is essential to address these challenges and to continuously improve model reliability and uptime.
What are popular job titles related to Ai Reliability Engineer jobs in Wisconsin? For Ai Reliability Engineer jobs in Wisconsin, the most frequently searched job titles are:
What job categories do people searching Ai Reliability Engineer jobs in Wisconsin look for? The top searched job categories for Ai Reliability Engineer jobs in Wisconsin are:
What cities in Wisconsin are hiring for Ai Reliability Engineer jobs? Cities in Wisconsin with the most Ai Reliability Engineer job openings:
Ai Reliability Engineer jobs near you
Senior DevOps Analyst

Senior DevOps Analyst

Madison Gas and Electric Company

Madison, WI • On-site

$131K - $168K/yr

Full-time

Re-posted 10 days ago

Job description

Position Purpose
Leads the design, implementation, and maturity of AI-enabled DevOps platforms, with a focus on Microsoft Copilot, GitHub Copilot, AI agents, and intelligent automation. Partners with technology and business stakeholders to enable scalable adoption of AI-assisted development and automation. Serves as a technical platform leader and advisor, integrating AI tools and agent-based solutions into Azure DevOps workflows and software development practices. Establishes standards, patterns, and best practices to ensure secure, reliable, and governed AI-enabled solutions while accelerating delivery through modern DevOps and continuous improvement.
Core Responsibilities
Note: This is not an all-inclusive listing
  • Design, build, deploy, and operate AI agents and intelligent automation solutions, including LLM-powered and multi-agent systems.
  • Lead the adoption of Microsoft Copilot, GitHub Copilot, and Copilot Studio to enable AI-assisted development and improve engineering productivity.
  • Serve as a technical advisor to DevOps, application, and platform teams on AI-enabled development, automation, and agent-based solutions.
  • Design and maintain Azure DevOps CI/CD pipelines supporting AI-assisted development, automated testing, and continuous delivery.
  • Partner with software engineering, enterprise architecture, and product teams to integrate AI-driven capabilities into application and platform solutions.
  • Architect and manage secure, scalable Azure infrastructure for AI workloads using infrastructure-as-code practices.
  • Establish and operationalize DevOps and MLOps practices, including versioning, monitoring, governance, and lifecycle management of AI systems.
  • Implement and maintain observability solutions (logging, metrics, tracing, and alerting) for distributed and AI-driven systems.
  • Define and enforce security, compliance, and governance standards across AI systems, pipelines, and cloud platforms.
  • Evaluate emerging AI and DevOps technologies to improve reliability, developer productivity, and operational efficiency.
  • Optimize scalability, performance, and cost for AI-enabled platforms and compute-intensive workloads.
Behavioral Competencies
Note: These are in addition to MGE's Core Competencies
  • Manages Complexity - Navigates sophisticated technical environments and ambiguous AI challenges effectively.
  • Drives Results - Consistently delivers high-quality, scalable solutions in fast-paced environments.
  • Collaborates - Builds strong partnerships across engineering, data science, and business teams.
  • Instills Trust - Gains credibility through technical expertise and reliable execution.
  • Strategic Mindset - Anticipates future AI and technology trends and aligns solutions with long-term objectives.
Skills
  • Strong experience building, operating, and enabling AI agents, intelligent automation, and AI-assisted development workflows.
  • Hands-on experience with Microsoft Copilot, GitHub Copilot, Copilot Studio, or similar AI-assisted engineering tools.
  • Deep mastery of Azure DevOps and modern DevOps practices, including CI/CD, Git, GitHub, infrastructure as code, automation, and platform reliability.
  • Expert-level experience within the Microsoft ecosystem, including Azure, Azure DevOps, GitHub Enterprise, and Azure-native services.
  • Strong understanding of LLM concepts, agent orchestration frameworks, and prompt engineering (e.g., LangChain, AutoGen, CrewAI).
  • Experience operationalizing AI and ML systems, including deployment, monitoring, governance, and lifecycle management.
  • Expertise in infrastructure as code using Terraform, Bicep, ARM templates, or similar frameworks.
  • Advanced experience with containerization and orchestration technologies such as Docker and Kubernetes.
  • Strong programming and automation skills using Python, PowerShell, Bash, or similar languages.
  • Experience implementing observability solutions using Azure Monitor, Log Analytics, Application Insights, or equivalent tools.
  • Advanced Git usage, branching strategies, and pull request workflows.
  • Strong understanding of security, identity, and compliance considerations in cloud and AI-enabled environments.
  • Proven ability to lead, enable, and influence DevOps and AI adoption across multiple teams.
Education
Bachelor's degree in Computer Science, Engineering, Information Technology, or a related field required; an equivalent combination of education and experience may be considered.
Relevant certifications preferred, such as:
  • Microsoft Certified: Azure DevOps Engineer Expert
  • Microsoft Certified: Azure AI Engineer Associate
  • Kubernetes certifications (CKA, CKAD)
  • Azure or GitHub architecture certifications
Experience
  • 7+ years of experience in DevOps, Platform Engineering, Site Reliability Engineering, or related roles supporting complex, cloud-based environments.
  • 3+ years of hands-on experience enabling or supporting AI-driven systems, intelligent automation, or AI-assisted development in production environments.
Work Location
  • This hybrid role is based at our Madison, WI headquarters. While three days onsite is the minimum, team collaboration and business needs may require additional in-office presence.
  • This position may require participation in on-call rotations and occasional off-hours support to ensure platform reliability and availability.

Pre-employment will require satisfactory completion of a background check and drug screen.
We are an AA/EOE employer and consider all qualified candidates without regard to protected status.

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