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Ai Reliability Engineer Jobs in Minnesota (NOW HIRING)

Brooksource

Associate Software Engineer

Minnetonka, MN · On-site

$30 - $35/hr

The role offers hands-on experience with application design, software development, automated testing, and Site Reliability Engineering (SRE) practices, leveraging AI and automation to drive ...

US Bank

Software Engineer 2 (AI/GenAI)

Hopkins, MN · On-site

... reliability engineering standards embedded into development standards - Embraces emerging ... Developed and deployed AI/GenAI-powered applications utilizing Large Language Models (LLMs ...

FAVARH

AI Principal Engineer

Minneapolis, MN · On-site +1

The AI Principal Engineer (IC Director) is responsible for leading the design, development, and ... Deep understanding of model lifecycle management, MLOps practices, and production reliability ...

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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 is an AI reliability engineer?

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.

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Cities in Minnesota with the most Ai Reliability Engineer job openings:

Ai Reliability Engineer jobs near you

Lead, AI Quality & Reliability Engineering

Vizient

Minneapolis, MN

$88K - $155K/yr

Full-time

Re-posted 29 days ago

Job description

When you're the best, we're the best. We instill an environment where employees feel engaged, satisfied and able to contribute their unique skills and talents while living and working as their authentic selves. We provide extensive opportunities for personal and professional development, building both employee competence and organizational capability to fuel exceptional performance through an inclusive environment both now and in the future.

Summary

In this role, you will lead the day-to-day execution and delivery of AI Quality Engineering initiatives supporting Vizient's enterprise AI transformation efforts. You will help ehelp implement AI Quality Engineering practices, AI validation processes, AI-assisted testing approaches, runtime quality controls, and scalable testing frameworks supporting responsible deployment of AI-powered business solutions. This role combines hands-on quality engineering leadership, AI-enabled testing modernization, healthcare workflow validation, team leadership, and cross-functional delivery coordination to ensure the reliability, performance, and governance of AI-enabled systems.

Responsibilities

  • Lead day-to-day AI Quality Engineering activities supporting AI-powered applications, LLM-enabled workflows, intelligent automation solutions, agentic systems, and enterprise AI platforms.
  • Implement AI Quality Engineering practices, including AI-native testing approaches, validation processes, runtime quality controls, reusable testing accelerators, and scalable testing workflows.
  • Coordinate AI validation efforts, including functional testing, prompt testing, workflow validation, regression testing, defect management, release readiness, and production quality assurance.
  • Partner with AI Engineering, AIOps, LLMOps, Security, Governance, Clinical, Data, and Product teams to ensure scalable and effective quality engineering processes across AI initiatives.
  • Support runtime quality and reliability through observability, monitoring, telemetry analysis, incident support, drift detection, distributed tracing, and continuous improvement initiatives.
  • Drive adoption of AI-assisted testing, intelligent automation, reusable test assets, and modern quality engineering practices that improve efficiency and quality outcomes.
  • Lead delivery coordination activities including sprint execution, testing planning, issue tracking, risk identification, dependency management, and release support.
  • Collaborate with clinical, operational, and engineering stakeholders to validate healthcare workflows, payer operations, and AI-enabled business processes while supporting responsible AI deployment.
  • Mentor quality engineers, analysts, contractors, and delivery teams while fostering a culture of continuous learning, engineering excellence, and operational accountability.

Qualifications

  • Bachelor's degree in Computer Science, Information Systems, Engineering, or related field preferred.
  • 7 or more years of experience in Quality Engineering, Software Testing, Quality Assurance, or related technology disciplines required.
  • 1 or more years of experience supporting AI Quality Engineering, AI testing, AI validation, AI-enabled automation, or modern AI engineering initiatives preferred.
  • Strong experience supporting Quality Engineering or Quality Assurance activities across enterprise platforms, APIs, operational workflows, healthcare applications, or integrated business systems required.
  • Experience with enterprise testing practices, test automation frameworks, SDLC methodologies, Agile delivery models, defect management, release validation, and production support coordination required.
  • Strong analytical, organizational, communication, collaboration, and leadership skills required.
  • Ability to manage multiple priorities within fast-paced, evolving, and highly collaborative enterprise environments required.
  • Experience with Python, APIs, automation frameworks, AI-enabled workflows, or modern software engineering practices preferred.
  • Experience supporting AI/ML platforms, orchestration frameworks, runtime monitoring, observability tooling, or AI operational support processes preferred.
  • Familiarity with AI-assisted testing, AI evaluation approaches, prompt testing, runtime observability, operational monitoring, drift detection, or AI operational assurance practices preferred.
  • Experience supporting enterprise modernization or transformation initiatives involving AI, automation, or operational scaling preferred.

#LI-JB1

Estimated Hiring Range:

At Vizient, we consider skills, experience, and organizational needs in our compensation approach. Geographic factors may adjust the range estimate and hires typically fall below the top range. Compensation decisions are tailored to individual circumstances. The current salary range for this role is $88,900.00 to $155,500.00.

This position is also incentive eligible.

Vizient has a comprehensive benefits plan! Please view our benefits here:

http://www.vizientinc.com/about-us/careers

Equal Opportunity Employer: Females/Minorities/Veterans/Individuals with Disabilities

The Company is committed to equal employment opportunity to all employees and applicants without regard to race, religion, color, gender identity, ethnicity, age, national origin, sexual orientation, disability status, veteran status or any other category protected by applicable law.

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