Senior Site Reliability Engineer

Towards the end of our interview process is an in-person interview. Do you want to help make the world safe from cyber attack?At Corelight, we believe that the best approach to cybersecurity risk starts with the network. Attackers can evade endpoint detection, firewalls and many other technologies - but they can’t avoid leaving digital footprints on the networks they traverse. Built on open-source innovations from Zeek, Suricata and YARA and refined through years of real-world use, Corelight transforms network footprints from physical, virtual and cloud networks into actionable insights. Our customers use these insights to speed incident response and proactively hunt for threats. We are at the forefront of network security, helping organizations protect their environments with an open NDR platform that delivers unparalleled network visibility. We are looking for a passionate and driven Pre-Sales Engineer to join our team, contributing to our mission of helping customers stay ahead of evolving threats. As a Senior Site Reliability Engineer, you will 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 Fueled by investments from top-tier venture capital organizations such as Crowdstrike, Accel and Insight, Corelight is the fastest growing network detection and response platform in the industry. Our customers trust us to protect mission-critical assets in leading enterprises, government, and research institutions worldwide. We are leading the way with AI-assisted workflows, machine learning models, cloud security and SaaS-based solutions to arm defenders with the tools and knowledge they need to disrupt cyber attacks. Our team of passionate innovators are dedicated to solving some of the toughest challenges in cybersecurity, while fostering a collaborative, inclusive, and growth-oriented culture. Corelight is committed to a geographically distributed yet connected employee base with employees working from home and office locations around the world. At Corelight, we are proud of our diversity of background and thought, and we’re united by our strong shared culture and values.We are looking forward to meeting you. Check us out at Notice of Pay Transparency:The compensation for this position may vary depending on factors such as your location, skills and experience. Depending on the nature and seniority of the role, a percentage of compensation may come in the form of a commission-based or discretionary bonus. Equity and additional benefits will also be awarded. Compensation Range$153,000—$188,000 USD #J-18808-Ljbffr