AI Reliability Engineer

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., * 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., * 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

• 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., * 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
• Cloud, Kubernetes, SRE, or DevOps certifications