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Ai Reliability Engineer Jobs in Reston, VA (NOW HIRING)

Transunion

Staff Site Reliability Engineer

Reston, VA

$59.25 - $78.75/hr

The Site Reliability Engineering team drives reliability strategy, elevates engineering standards ... Hands-on experience designing and integrating AI/ML-powered solutions into cloud-native platforms ...

TransUnion

Staff Site Reliability Engineer

Reston, VA · On-site

$59.25 - $78.75/hr

The Site Reliability Engineering team drives reliability strategy, elevates engineering standards ... Hands-on experience designing and integrating AI/ML-powered solutions into cloud-native platforms ...

Trans Union LLC

Staff Site Reliability Engineer

Reston, VA

$59.25 - $78.75/hr

The Site Reliability Engineering team drives reliability strategy, elevates engineering standards ... Hands-on experience designing and integrating AI/ML-powered solutions into cloud-native platforms ...

Commence

Sr Site Reliability Engineer

Leesburg, VA · On-site

$145K - $175K/yr

As a Senior Site Reliability Engineer at Commence, you will own the reliability, scalability, and ... Exposure to AI/ML infrastructure and the reliability challenges unique to model serving.

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Ai Reliability Engineer information

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How much do ai reliability engineer jobs pay per year?

As of Jun 8, 2026, the average yearly pay for ai reliability engineer in Reston, VA is $122,734.00, according to ZipRecruiter salary data. Most workers in this role earn between $106,600.00 and $134,200.00 per year, depending on experience, location, and employer.

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 is a $900,000 AI job?

A $900,000 AI job typically refers to highly senior roles such as AI executives, chief AI officers, or lead AI engineers at top technology companies, often involving advanced expertise in machine learning, deep learning, and AI strategy. These positions usually require extensive experience, specialized skills, and may include performance-based bonuses or stock options that contribute to the high total compensation.

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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Ai Reliability Engineer jobs near you
Sr. Site Reliability Engineer

Sr. Site Reliability Engineer

Tiger Analytics Inc.

Washington, DC • On-site

$64.50 - $85.75/hr

Full-time

Posted yesterday

Job description

Role Overview

We are seeking a high-caliber Site Reliability Engineer (SRE) to join our Forward Engineering team. You will be the guardian of our production ecosystems, ensuring that our complex, data-driven AI platforms remain resilient, scalable, and highly performant. This role is a hybrid of software engineering and systems architecture, with a specialized focus on MLOps—bridging the gap between model development and production-grade reliability.

Key Responsibilities1. Reliability & Performance Engineering
  • SLA/SLO Management: Define, monitor, and maintain Service Level Objectives (SLOs) and Service Level Indicators (SLIs) for critical AI/ML services.
  • Error Budgeting: Manage error budgets to balance the velocity of feature releases from the ML team with the stability of the production environment.
  • Scalability: Architect and manage auto-scaling strategies for Kubernetes (GKE) to handle fluctuating workloads during model training and high-volume inference.
2. MLOps & AI Infrastructure
  • Model Serving Reliability: Ensure the high availability of Vertex AI endpoints and custom inference services.
  • GPU/TPU Optimization: Monitor and optimize compute resource utilization (accelerators) to ensure cost-efficient performance for Large Language Models (LLMs).
  • Pipeline Resilience: Support and stabilize ML pipelines (Vertex AI Pipelines/Kubeflow) to ensure seamless data flow from ingestion to model retraining.
3. Automation & Orchestration (Eliminating "Toil")
  • Infrastructure as Code (IaC): Use Terraform or Pulumi to provision and manage consistent, version-controlled cloud environments.
  • CI/CD & GitOps: Design and optimize robust deployment pipelines for both application code and ML models using GitHub Actions, Cloud Build, or ArgoCD.
  • Task Automation: Develop custom Python or Go scripts to automate repetitive operational tasks, self-healing mechanisms, and resource cleanup.
4. Monitoring, Alerting & Incident Response
  • Observability: Build and manage comprehensive dashboards using Prometheus, Grafana, or Google Cloud Operations Suite (Stackdriver).
  • Incident Management: Act as a primary responder in on-call rotations, leading the technical resolution of production outages.
  • Blameless Post-Mortems: Conduct deep-dive root cause analysis (RCA) to ensure systemic issues are identified and permanently remediated through code.

Requirements

Orchestration: Expert-level knowledge of Kubernetes (K8s) and Docker.

MLOps Stack: Familiarity with tools such as Kubeflow, Vertex AI, MLflow, or DVC.

Scripting: Strong proficiency in Python (for automation) and Bash; knowledge of Go is a plus.

Data Systems: Experience managing the reliability of data-heavy services (BigQuery, Pub/Sub, or Vector Databases like Pinecone/Milvus).

Networking: Solid understanding of VPCs, Load Balancers, DNS, and secure service mesh (Istio/Anthos).

Benefits

Benefits

Significant career development opportunities exist as the company grows. The position offers a unique opportunity to be part of a small, fast-growing, challenging and entrepreneurial environment, with a high degree of individual responsibility.

Tiger Analytics provides equal employment opportunities to applicants and employees without regard to race, color, religion, age, sex, sexual orientation, gender identity/expression, pregnancy, national origin, ancestry, marital status, protected veteran status, disability status, or any other basis as protected by federal, state, or local law.