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Ai Reliability Engineer Jobs in Texas (NOW HIRING)

Site Reliability Engineer

Austin, TX · On-site

$56.50 - $75/hr

Future Secure AI is building innovative solutions at the forefront of AI technology, seeking a Site Reliability Engineer to design, build, and operate the platforms that power AI Co-Workers. The role ...

Site Reliability Engineer

Austin, TX · On-site

$56.50 - $75/hr

Future Secure AI is building innovative solutions at the forefront of AI technology. They are seeking a Site Reliability Engineer to design, build, and operate platforms that support AI Co-Workers ...

Site Reliability Engineer

Austin, TX · On-site

$56.50 - $75/hr

Future Secure AI is at the forefront of AI technology, tackling significant real-world challenges for global enterprises. They are seeking a Site Reliability Engineer to design, build, and operate ...

Site Reliability Engineer

Austin, TX

$56.50 - $75/hr

About the Role We are looking for a Site Reliability Engineer to help design, build, and operate the platforms that power AI CoWorkers. This is a handson role for an engineer who enjoys owning ...

Follow Shield AI on LinkedIn, X, Instagram, and YouTube. As a Hardware Reliability Engineer at Shield AI, you will be responsible for ensuring the robustness and long-term performance of our VBAT ...

Site Reliability Engineer

Austin, TX · On-site

$56.50 - $75/hr

We work at the frontier of AI, tackling big, real-world problems for global enterprises across ... This is a hands-on role for an engineer who enjoys owning reliability end-to-end and working ...

Reliability Engineer

Fort Worth, TX · On-site

$98K - $123K/yr

Reliability Engineer This critical role within the Celestica Global Quality Organization is ... Exposure to Generative AI and Large Language Models (LLMs) for application in data analysis ...

Define and lead WEX's AI-Powered Reliability Engineering strategy, driving adoption of SRE agents across the software lifecycle-from design and development through deployment and operations, to ...

Define and lead WEX's AI-Powered Reliability Engineering strategy, driving adoption of SRE agents across the software lifecycle-from design and development through deployment and operations, to ...

They are seeking an Engineer III - AI to lead technical efforts on AI reliability, bias mitigation, and drift detection while designing intelligent systems and workflows. Responsibilities : • ...

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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 cities in Texas are hiring for Ai Reliability Engineer jobs? Cities in Texas with the most Ai Reliability Engineer job openings:
Infographic showing various Ai Reliability Engineer job openings in Texas as of July 2026, with employment types broken down into 100% Contract. Highlights an 100% In-person job distribution.

Site Reliability Engineer

Future Secure AI

Austin, TX • On-site

$56.50 - $75/hr

Full-time

Posted 11 days ago


Job description

Job Summary:
Future Secure AI is building innovative solutions at the forefront of AI technology, seeking a Site Reliability Engineer to design, build, and operate the platforms that power AI Co-Workers. The role involves owning production infrastructure, enhancing system reliability, and collaborating with engineering teams to ensure robust and scalable systems.
Responsibilities:
• Design, build, and operate reliable production infrastructure supporting AI Co‑Workers
• Own Kubernetes‑based platforms used to deploy and run AI workloads
• Build and maintain infrastructure as code using Terraform
• Implement and maintain Helm‑based deployment workflows
• Define, measure, and improve system reliability using SLIs, SLOs, and SLAs
• Participate in on‑call rotation, incident response, root cause analysis, and post‑mortems
• Reduce operational toil through automation and engineering improvements
• Build and improve observability across monitoring, logging, and alerting
• Partner closely with engineers to ensure systems are resilient, scalable, and secure
• Operate across build, deploy, and operate phases of the software lifecycle
Qualifications:
Required:
• Hands‑on Kubernetes experience designing, building, or operating workloads on EKS, AKS, GKE, or self‑managed Kubernetes
• Hands‑on Terraform experience for infrastructure provisioning and automation
• Hands‑on Helm experience for Kubernetes application deployment
• Professional experience using at least two programming or scripting languages such as Python, Go, Java, Bash, PowerShell, or Ruby
• Direct Site Reliability Engineer experience or equivalent, including reliability engineering, on‑call, incident response, post‑mortems, and toil reduction
Preferred:
• Relevant certifications such as CKA, CKAD, cloud certifications, DevOps, DevSecOps, or programming credentials
Company:
Future Secure AI develops secure, bespoke AI Co-Workers and multi-agent systems for complex enterprise workflows. Founded in , the company is headquartered in Austin, USA, with a team of 201-500 employees. The company is currently Growth Stage.