DevOps Specialist Engineer – SRE, Cloud & Applied AI

Company Overview

Position Overview

Responsibilities

• Design, build, operate, and continuously improve large-scale cloud-native production systems.
• Define and own SLIs, SLOs, SLAs, error budgets, and reliability objectives.
• Lead production incident response, on-call operations, root-cause analysis, and reliability improvements.
• Build and manage CI/CD pipelines, Kubernetes platforms, infrastructure automation, and multi-environment deployments.
• Implement Infrastructure as Code using Terraform and deployment automation using tools such as ArgoCD.
• Develop production-grade observability across metrics, logging, and distributed tracing.
• Work with technologies such as Prometheus, Grafana, OpenTelemetry, Datadog, Dynatrace, Splunk, Azure Monitor, and AWS CloudWatch.
• Operate AI/ML and GenAI workloads in production, addressing reliability, performance, model drift, output variance, and train/serve skew.
• Support MLOps/LLMOps platforms and AI control-plane capabilities such as model gateways and guardrails.
• Implement Kubernetes/Docker-based solutions and cloud-native networking across multiple environments.
• Conduct load and performance testing using tools such as LoadRunner, k6, or JMeter.
• Implement chaos engineering, capacity planning, autoscaling, and resilience testing.

Skills & Experience

• 6–9 years of experience in DevOps, SRE, Site Reliability Engineering, Platform Engineering, or Software Engineering.
• Bachelor's degree in Computer Science, Software Engineering, Data Science, Machine Learning, or a related discipline.
• Strong programming experience in one or more of Python, C#/.NET, Go, Java, or Bash.
• Strong hands-on experience with Azure, AWS, or GCP; Azure/AWS preferred.
• Experience with Kubernetes, Docker, Terraform, and cloud-native architectures.
• Strong understanding of CI/CD, GitHub, Azure DevOps (ADO), and ArgoCD.
• Experience with production observability and monitoring using tools such as Prometheus, Grafana, OpenTelemetry, Datadog, Dynatrace, Splunk, CloudWatch, or Azure Monitor.
• Strong understanding of SRE principles, SLIs, SLOs, SLAs, error budgets, incident management, and production on-call operations.
• 3+ years of experience operating or supporting large-scale production systems.
• Experience with AI/ML or GenAI workloads in production and familiarity with Azure OpenAI, AWS Bedrock, or Vertex AI.
• Knowledge of MLOps/LLMOps, MLflow, LangFuse, LangSmith, or equivalent AI/agent orchestration platforms.
• Experience with load/performance testing, capacity planning, autoscaling, and chaos engineering.