Corelight
Senior Site Reliability Engineer
role at
Corelight
We are looking for a Senior Site Reliability Engineer to design, automate, and scale cloud and hybrid platforms that power AI/ML workloads and SaaS services. You\'ll collaborate with engineering teams to build reliable, secure, and observable infrastructure, manage Kubernetes environments, and enable CI/CD pipelines for continuous delivery of AI models and applications at scale. Your expertise in cloud, DevOps, and MLOps will drive performance, uptime, and innovation across production systems.
Responsibilities
Design, deploy, and scale AI/ML/LLM infrastructure across cloud platforms (AWS, Azure, or GCP) ensuring high reliability and performance.
Manage and optimize Kubernetes environments (EKS, AKS, GKE) for AI services, data pipelines, and model operations.
Build and automate end-to-end data and model pipelines for fine-tuning, inference, and RAG workloads using Terraform, Python, and CI/CD tooling.
Utilize automation tools such as GitOps, CI/CD pipelines, and containerization technologies (Docker, Kubernetes) to streamline ML/LLM tasks across the Large Language Model lifecycle.
Implement monitoring, observability, and reliability best practices using Prometheus, Grafana, ELK/EFK, Langfuse, and SLI/SLO/SLA frameworks.
Lead incident response, performance tuning, and cost optimization across AI infrastructure and production workloads.
Minimum Qualifications
Bachelor\'s or Master\'s degree in Computer Science, Engineering, or related field, or equivalent experience.
6+ years in SRE, DevOps, Platform Engineering, MLOps, or Cloud Infrastructure roles.
3+ years building software infrastructure in a distributed systems architecture environment.
3+ years of production experience with Kubernetes (EKS, GKE, AKS) and containerization tools like Docker.
Strong programming skills in Python and proficiency in Bash, Go, or PowerShell.
Proficiency with Infrastructure-as-Code tools (Terraform, CloudFormation).
Experience with Kubernetes Operators, Helm, GitOps (ArgoCD, Flux), or Service Mesh (Istio, Linkerd).
Exposure to serverless compute (AWS Lambda, Azure Functions).
Experience building or automating data and model pipelines for AI/ML/LLM workloads (e.g., RAG, fine-tuning, inference).
Strong understanding of observability and monitoring using Prometheus, Grafana, ELK/EFK, Langfuse, or similar platforms.
Familiarity with SLI/SLO/SLA practices, incident response, and reliability engineering in production environments.
Nice to Have
Cloud certifications (AWS, Azure, or GCP – e.g., Solutions Architect, DevOps Engineer).
Experience with agentic AI frameworks (CrewAI, LangGraph, AutoGen)
Work with vector databases and RAG frameworks (Pinecone, Weaviate, Chroma).
Background in hybrid or on-prem AI deployments, including OpenShift or Rancher.
Familiarity with configuration management (Ansible, Chef, Puppet).
Contributions to open-source AI/ML, DevOps, or platform tooling.
Experience with multimodal AI or model observability platforms (RAGAS, AgentOps, Langtrace), Distributed Tracing, OpenTelemetry
Knowledge of performance tuning, cost efficiency, or capacity planning for AI/LLM infrastructure.
Understanding of security controls and FedRAMP compliance for cloud and various workloads.
Compensation information: The compensation for this position may vary depending on factors such as location, skills and experience. Equity and additional benefits will also be awarded. Compensation Range: $142,000—$176,000 USD
We are looking forward to connecting with you. For more information, visit www.corelight.com
#J-18808-Ljbffr
role at
Corelight
We are looking for a Senior Site Reliability Engineer to design, automate, and scale cloud and hybrid platforms that power AI/ML workloads and SaaS services. You\'ll collaborate with engineering teams to build reliable, secure, and observable infrastructure, manage Kubernetes environments, and enable CI/CD pipelines for continuous delivery of AI models and applications at scale. Your expertise in cloud, DevOps, and MLOps will drive performance, uptime, and innovation across production systems.
Responsibilities
Design, deploy, and scale AI/ML/LLM infrastructure across cloud platforms (AWS, Azure, or GCP) ensuring high reliability and performance.
Manage and optimize Kubernetes environments (EKS, AKS, GKE) for AI services, data pipelines, and model operations.
Build and automate end-to-end data and model pipelines for fine-tuning, inference, and RAG workloads using Terraform, Python, and CI/CD tooling.
Utilize automation tools such as GitOps, CI/CD pipelines, and containerization technologies (Docker, Kubernetes) to streamline ML/LLM tasks across the Large Language Model lifecycle.
Implement monitoring, observability, and reliability best practices using Prometheus, Grafana, ELK/EFK, Langfuse, and SLI/SLO/SLA frameworks.
Lead incident response, performance tuning, and cost optimization across AI infrastructure and production workloads.
Minimum Qualifications
Bachelor\'s or Master\'s degree in Computer Science, Engineering, or related field, or equivalent experience.
6+ years in SRE, DevOps, Platform Engineering, MLOps, or Cloud Infrastructure roles.
3+ years building software infrastructure in a distributed systems architecture environment.
3+ years of production experience with Kubernetes (EKS, GKE, AKS) and containerization tools like Docker.
Strong programming skills in Python and proficiency in Bash, Go, or PowerShell.
Proficiency with Infrastructure-as-Code tools (Terraform, CloudFormation).
Experience with Kubernetes Operators, Helm, GitOps (ArgoCD, Flux), or Service Mesh (Istio, Linkerd).
Exposure to serverless compute (AWS Lambda, Azure Functions).
Experience building or automating data and model pipelines for AI/ML/LLM workloads (e.g., RAG, fine-tuning, inference).
Strong understanding of observability and monitoring using Prometheus, Grafana, ELK/EFK, Langfuse, or similar platforms.
Familiarity with SLI/SLO/SLA practices, incident response, and reliability engineering in production environments.
Nice to Have
Cloud certifications (AWS, Azure, or GCP – e.g., Solutions Architect, DevOps Engineer).
Experience with agentic AI frameworks (CrewAI, LangGraph, AutoGen)
Work with vector databases and RAG frameworks (Pinecone, Weaviate, Chroma).
Background in hybrid or on-prem AI deployments, including OpenShift or Rancher.
Familiarity with configuration management (Ansible, Chef, Puppet).
Contributions to open-source AI/ML, DevOps, or platform tooling.
Experience with multimodal AI or model observability platforms (RAGAS, AgentOps, Langtrace), Distributed Tracing, OpenTelemetry
Knowledge of performance tuning, cost efficiency, or capacity planning for AI/LLM infrastructure.
Understanding of security controls and FedRAMP compliance for cloud and various workloads.
Compensation information: The compensation for this position may vary depending on factors such as location, skills and experience. Equity and additional benefits will also be awarded. Compensation Range: $142,000—$176,000 USD
We are looking forward to connecting with you. For more information, visit www.corelight.com
#J-18808-Ljbffr