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Ai Reliability Engineer Jobs in Missouri (NOW HIRING)

Jobgether

$44 - $58.50/hr

Our partner is looking for a Site Reliability Engineer based in Netherlands. This is a senior ... You will also help shape how AI-assisted tools are used for automation, incident analysis, runbooks ...

Jobgether

$44 - $58.50/hr

Background working in AI/ML, geospatial technology, or similarly data-intensive environments is a ... Opportunity to contribute to reliability engineering practices involving SLOs, SLIs, error budgets ...

Jobgether

$44 - $58.50/hr

A key focus will be productionizing AI workloads, standardizing customer environments, and ... Lead reliability initiatives across multiple engineering streams and establish consistent SRE ...

Jobtailor

Staff Site Reliability Engineer

California, MO · On-site

$140 - $210/hr

Design, build, and maintain highly available, scalable, and secure infrastructure to support our AI ... Core Engineering: 10+ years of experience in SRE, DevOps, or Software/Systems Engineering ...

Jobgether

$44 - $58.50/hr

Exposure to AI, ML, LLM, or other advanced technology platforms is a plus. * Experience ... Opportunity to establish and lead an SRE function and shape reliability standards across the ...

Jobgether

$44 - $58.50/hr

Our partner is looking for a Senior Platform Engineer (SRE) based in Netherlands ... This is a senior infrastructure role at the heart of a high-scale, AI-powered sports media platform.

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Ai Reliability Engineer information

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 Missouri?

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

What job categories do people searching Ai Reliability Engineer jobs in Missouri look for?

The top searched job categories for Ai Reliability Engineer jobs in Missouri are:

What cities in Missouri are hiring for Ai Reliability Engineer jobs?

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

Ai Reliability Engineer jobs near you

Site Reliability Engineer

Jobgether

On-site

$44 - $58.50/hr

Full-time

Medical

Posted 15 days ago

Job description

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Site Reliability Engineer based in Netherlands.

This is a senior reliability engineering role focused on defining and advancing the infrastructure standards behind a globally scaled, AI-native platform.
You will take ownership of reliability strategy across production infrastructure, with a particular focus on AWS, Kubernetes, event-driven systems, and AI agent workloads.
The role combines deep hands-on engineering with architectural leadership, incident management, observability, and technical mentorship.
You will design systems that remain resilient under increasing transaction volumes while establishing measurable standards for reliability across engineering teams.
A key part of the role will be evolving synchronous architectures toward durable asynchronous communication and strengthening the platform through resilience testing and chaos engineering.
You will also help shape how AI-assisted tools are used for automation, incident analysis, runbooks, and root-cause investigations.
Success means becoming the trusted technical authority for complex reliability decisions while creating practices that make reliability scalable across the organization.

Accountabilities
  • Define and implement the reliability strategy across the platform, including SLOs, SLIs, error budgets, incident practices, and reliability standards adopted by engineering teams.
  • Drive major architectural decisions as infrastructure evolves, evaluating technologies and designing systems that remain scalable, resilient, observable, and maintainable.
  • Design and own event-driven communication and messaging infrastructure, including the transition from synchronous patterns to durable asynchronous architectures.
  • Manage and evolve cloud infrastructure on AWS, using Infrastructure as Code to automate provisioning, configuration, deployment, and operational processes.
  • Ensure Kubernetes and containerized workloads scale reliably as transaction volumes and AI workloads increase.
  • Build and maintain comprehensive observability through monitoring, dashboards, alerting, application performance monitoring, and distributed tracing.
  • Serve as the senior escalation point for complex production incidents, leading incident response, root-cause investigations, and blameless postmortems.
  • Turn incident findings into permanent improvements through architectural changes, automation, operational controls, and resilience patterns.
  • Establish a continuous chaos engineering and resilience testing practice through fault injection, game days, and controlled failure experiments.
  • Mentor senior and mid-level engineers while raising the technical bar for reliability engineering and influencing engineering practices across teams.
  • Use AI-assisted tooling for automation, runbooks, incident analysis, and root-cause investigations, while helping establish effective AI-enabled engineering practices.
  • Within the first 6-12 months, establish the platform reliability strategy, lead at least one major architectural evolution, and drive adoption of the SLO and error-budget framework across engineering teams.
Requirements
  • Extensive experience in Site Reliability Engineering, Platform Engineering, DevOps, or a closely related discipline, with demonstrated ownership of production-scale systems.
  • Deep expertise in event-driven architecture and messaging systems such as Kafka, NATS, or RabbitMQ, including at-least-once delivery, consumer groups, dead-letter queues, backpressure, and migrations from synchronous to asynchronous architectures.
  • Strong AWS expertise across services such as EC2, VPC, IAM, S3, and RDS, combined with solid networking fundamentals.
  • Hands-on Infrastructure as Code experience using Terraform, Pulumi, or similar tools, with infrastructure managed through version-controlled workflows and code reviews.
  • Strong production experience with Kubernetes and Docker, including container lifecycle management, resource limits, health checks, and orchestration at scale.
  • Proven observability expertise using Datadog or equivalent platforms, including dashboards, monitoring, APM, distributed tracing, and alerting.
  • Demonstrated experience defining and operating SLOs, SLIs, and error budgets across multiple services.
  • Hands-on experience with chaos engineering, fault injection, game days, or resilience experiments using tools such as Gremlin, Chaos Mesh, AWS FIS, or similar technologies.
  • Strong distributed systems debugging skills, with experience diagnosing asynchronous workflows, cascading failures, and complex production incidents.
  • Ability to code for automation and engineering tooling using Go, Python, or a similar programming language.
  • Solid database knowledge across SQL and NoSQL technologies, particularly PostgreSQL, MongoDB, and Redis, including indexing, replication, and performance optimization.
  • Proven technical leadership experience, including setting reliability standards, influencing architecture across teams, and mentoring engineers.
  • Advanced written and spoken English communication skills.
  • Experience with AI or MLOps infrastructure, including model serving, LLM inference, GPU/resource management, or AI agent observability, is highly advantageous.
  • Familiarity with multi-tenant container platforms and customer workload infrastructure is a plus.
  • Experience with data pipelines and orchestration tools such as Airflow or Prefect, and data platforms such as Databricks, Snowflake, or BigQuery, is beneficial.
  • Familiarity with incident management platforms such as PagerDuty, Opsgenie, or incident.io is an advantage.
  • Experience in the payments industry is preferred.
  • Additional experience with ECS, s6-overlay, AI agent frameworks, or Spanish proficiency is a plus.
Benefits
  • Competitive compensation.
  • Fully remote working environment with the flexibility to work from different locations.
  • One-time home office allowance to help create an effective workspace.
  • Company-provided work equipment.
  • Stock options.
  • Health plan available wherever you are.
  • Flexible days off.
  • Access to language, professional, and personal development courses.
  • Opportunity to work on globally scaled infrastructure supporting complex payment and AI workloads.
  • Significant technical ownership and influence over reliability strategy, architecture, and engineering standards.
  • Collaborative international environment with opportunities to mentor engineers and shape organization-wide engineering practices.
How Jobgether works:
We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.
We appreciate your interest and wish you the best!
 Why Apply Through Jobgether? 
 
Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.
 
 
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We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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