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Ai Reliability Engineer Jobs in Hialeah, FL (NOW HIRING)

OpenEvidence

Software Engineer, Site Reliability

Miami, FL · On-site +1

$54.50 - $72.50/hr

Role As a Software Engineer working on Site Reliability at OpenEvidence, you will build and harden the mission-critical infrastructure powering our medical AI platform used by healthcare providers ...

Inficare Technologies

AI Engineer

Sunrise, FL · On-site

AI Engineer Location: Sunrise, FL Job Type: Contract (Long Term) Experience: 5+ years Skills ... Ensure AI systems meet enterprise standards for reliability, explainability, observability, and ...

Worth AI

Senior Agentic (AI) Engineer

Miami, FL · Remote

$107K - $146K/yr

Worth AI is hiring a Senior Agentic AI Engineer to design and ship production agent systems that ... Production MLOps fluency: deployed LLM workloads under real latency, cost, and reliability ...

Worth AI

Senior Agentic (AI) Engineer

Miami, FL · On-site +1

$99K - $137K/yr

Worth AI is hiring a Senior Agentic AI Engineer to design and ship production agent systems that ... Production MLOps fluency: deployed LLM workloads under real latency, cost, and reliability ...

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All JobsAi Reliability Engineer JobsAi Reliability Engineer Jobs in Hialeah, FL

Ai Reliability Engineer information

See Hialeah, FL salary details

$55.2K

$106.8K

$127.7K

How much do ai reliability engineer jobs pay per year?

As of Aug 25, 2026, the average yearly pay for ai reliability engineer in Hialeah, FL is $106,843.00, according to ZipRecruiter salary data. Most workers in this role earn between $92,800.00 and $116,800.00 per year, depending on experience, location, and employer.

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.

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 popular job titles related to Ai Reliability Engineer jobs in Hialeah, FL?

For Ai Reliability Engineer jobs in Hialeah, FL, the most frequently searched job titles are:

What cities near Hialeah, FL are hiring for Ai Reliability Engineer jobs?

Cities near Hialeah, FL with the most Ai Reliability Engineer job openings:

Ai Reliability Engineer jobs near you

DevOps Engineer - AI Model Evaluator - AI Trainer

Miami, FL • Remote

Mercor
See more jobs at Mercorexternal

$85/hr

Full-time

Posted 15 days ago

Job description

About the job

Mercor connects elite creative and technical talent with leading AI research labs. Headquartered in San Francisco, our investors include Benchmark, General Catalyst, Peter Thiel, Adam D'Angelo, Larry Summers, and Jack Dorsey.

Position: DevOps / SRE / Cloud Engineer (Coding Agent Experience)
Type: Contract
Compensation: $85/hour
Location: Remote

Role Responsibilities

  • Use frontier AI coding agents to complete and evaluate complex infrastructure engineering tasks.
  • Review model-generated implementations involving cloud platforms, Kubernetes, CI/CD systems, observability, and infrastructure automation.
  • Identify bugs, edge cases, reliability issues, and failure modes in model outputs.
  • Compare outputs from multiple frontier models to assess their strengths and weaknesses.
  • Apply professional engineering judgment to realistic infrastructure engineering scenarios.

Qualifications

Must-Have

  • 2+ years of professional DevOps, SRE, or Cloud Engineering experience.
  • Experience with AWS, Azure, GCP, Kubernetes, Terraform, CI/CD pipelines, or observability tooling.
  • Regular use of AI coding agents such as Cursor, Claude Code, Codex, Windsurf, Gemini CLI, or similar tools.
  • Ability to evaluate model-generated infrastructure and reliability engineering solutions.

Preferred

  • Experience supporting production-scale systems.

Compensation & Legal

  • $400 per accepted task
  • Compensation tied to accepted work.

Application Process (Takes 20–30 mins to complete)

  • Upload resume
  • AI interview based on your resume
  • Submit form

Resources & Support

  • For details about the interview process and platform information, please check: https://talent.docs.mercor.com/welcome
  • For any help or support, reach out to: support@mercor.com

PS: Our team reviews applications daily. Please complete your AI interview and application steps to be considered for this opportunity.


#hiringmercor