Bachelor's or Master's degree in Computer Science, Engineering, or related field.
4+ years in SRE, DevOps, Platform or Cloud Engineering, or MLOps.
Experience with AI or machine learning in production.
Strong programming skills in Python, Go, or Bash.
Hands-on experience with Linux administration.
Familiarity with Docker and Kubernetes.
Experience with AWS, Azure, or Google Cloud Platform.
Responsibilities
Design and implement reliability engineering practices for AI platforms.
Monitor availability, latency, throughput, and health of AI services.
Develop SLIs, SLOs, and SLAs for AI systems.
Build automated monitoring, alerting, and self-healing capabilities.
Collaborate with cross-functional teams to enhance production stability.
Automate tasks using scripting and Infrastructure as Code.
Support deployment and rollback strategies for AI services.
Benefits
Hybrid, remote, or on-site work options.
Opportunity to work with state-of-the-art AI models.
Collaboration with diverse teams of engineers and data scientists.
Focus on continuous improvement and operational excellence.
Full Job Description
Job Title: AI Reliability Engineer
Job Summary
We are seeking an AI Reliability Engineer to ensure the reliability, availability, scalability, and operational excellence of AI and machine learning systems in production. This role combines Site Reliability Engineering (SRE), MLOps, and cloud engineering practices to build resilient AI platforms, monitor model performance, automate operations, and improve system reliability. The ideal candidate has experience supporting production AI workloads, cloud-native infrastructure, observability, incident management, and automation.
Key Responsibilities
Design and implement reliability engineering practices for AI and machine learning platforms.
Monitor the availability, latency, throughput, and health of AI services and model inference endpoints.
Develop Service Level Indicators (SLIs), Service Level Objectives (SLOs), and Service Level Agreements (SLAs) for AI systems.
Build automated monitoring, alerting, incident response, and self-healing capabilities.
Improve the reliability, scalability, and resilience of AI infrastructure and model-serving platforms.
Collaborate with AI Engineers, Data Scientists, Platform Engineers, DevOps Engineers, and Software Engineers to enhance production stability.
Automate operational tasks using scripting and Infrastructure as Code (IaC).
Support deployment, rollback, and release strategies for AI services.
Investigate production incidents, conduct root cause analysis (RCA), and implement preventive measures.
Monitor model performance, data quality, model drift, and inference reliability.
Optimize cloud infrastructure, GPU utilization, and resource efficiency.
Implement disaster recovery, backup, failover, and business continuity strategies.
Ensure compliance with security, governance, and operational best practices.
Develop operational dashboards, runbooks, and reliability metrics.
Required Qualifications
Bachelor's or Master's degree in Computer Science, Engineering, Information Technology, or a related field.
4+ years of experience in Site Reliability Engineering (SRE), DevOps, Platform Engineering, Cloud Engineering, or MLOps.
Experience supporting AI or machine learning applications in production.
Strong programming skills in Python, Go, or Bash.
Hands-on experience with Linux administration.
Experience with Docker and Kubernetes.
Experience with AWS, Microsoft Azure, or Google Cloud Platform.
Experience with CI/CD tools such as GitHub Actions, GitLab CI, Azure DevOps, or Jenkins.
Experience with Infrastructure as Code tools such as Terraform or Pulumi.
Strong understanding of distributed systems, networking, and cloud architecture.
Experience with monitoring and observability platforms such as Prometheus, Grafana, OpenTelemetry, ELK Stack, Datadog, or Splunk.
Preferred Qualifications
Experience with MLOps platforms such as MLflow, Kubeflow, SageMaker, Vertex AI, or Azure Machine Learning.
Experience supporting Large Language Models (LLMs) and Generative AI applications.
Knowledge of model serving technologies such as KServe, NVIDIA Triton Inference Server, Ray Serve, or BentoML.
Experience implementing AI model monitoring, drift detection, and performance analytics.
Familiarity with vector databases and Retrieval-Augmented Generation (RAG) architectures.
Experience with GPU infrastructure and inference optimization.
Knowledge of chaos engineering and resilience testing.
Understanding of Responsible AI, governance, and operational compliance.
Technical Skills
Python
Go
Bash
Linux
Docker
Kubernetes
Terraform / Pulumi
Git
GitHub Actions
GitLab CI
Azure DevOps
Jenkins
AWS / Azure / Google Cloud Platform
Prometheus
Grafana
OpenTelemetry
ELK Stack
Datadog
Splunk
MLflow
Kubeflow
SageMaker
Vertex AI
Azure Machine Learning
KServe
NVIDIA Triton Inference Server
Ray Serve
BentoML
PostgreSQL
Redis
Soft Skills
Strong analytical and troubleshooting skills
Excellent communication and cross-functional collaboration
Strong incident management and problem-solving abilities
Ownership and accountability
Attention to detail
Continuous improvement mindset
Nice to Have
Experience defining and managing SLOs, SLIs, and error budgets for AI services
Experience implementing automated remediation and self-healing systems
Knowledge of FinOps and AI infrastructure cost optimization
Experience with multi-cloud or hybrid cloud deployments
