Lead Cloud Infrastructure Engineer​/Site Reliability Engineer; SRE

Be part of the team that defends the networks the world depends on

Corelight defends the world's most sensitive networks—from global commerce to national defense—quietly, relentlessly, and with resolve. As cyber threats grow faster and smarter, we serve as the trusted force behind network resilience, putting elite defense within reach.

By transforming digital footprints from physical, virtual, and cloud networks into actionable insights, we empower defenders to illuminate blind spots and stay ahead of an evolving threat landscape. Built on open‑source innovations and fueled by industry‑leading agentic AI technology, Corelight helps teams detect advanced threats and close cases with unprecedented clarity and precision.

As a Lead Cloud Infrastructure Engineer / Site Reliability Engineer (SRE), you will ensure the stability, performance, and security of our Federal region’s cloud platform. You'll manage infrastructure and operations with a focus on availability, latency, performance optimization, monitoring, incident response, and capacity planning. This role requires maintaining a FedRAMP‑compliant environment and working closely with teams to meet the highest standards of security and compliance.

We adopt an "everything as code" approach, leveraging automation and best practices to create an efficient, reliable, and scalable infrastructure. You will be instrumental in maintaining core infrastructure services that are robust, secure, and capable of processing high volumes of data seamlessly.

• Collaborate with software engineering teams to ensure the reliability, performance, and security of the Federal region’s infrastructure.
• 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 Git Ops, 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.
• Participate in 24x7 on‑call rotations, leading incident response, performance tuning, and cost optimization across SaaS Platform and production workloads.
• Own infrastructure end to end, leading scaling initiatives, deployments, and automation, and providing technical leadership across the team.

• Bachelor's or Master's degree in Computer Science, Engineering, or related field, or equivalent experience.
• 8+ years in SRE, Dev Ops, Platform Engineering, MLOps, or Cloud Infrastructure roles.
• 4+ years of production experience with Kubernetes (EKS, GKE, AKS) and containerization tools like Docker.
• Strong programming skills in Python and proficiency in Zyphyrscript, Bash, Go, or Power Shell.
• Proficiency with Infrastructure‑as‑Code tools (Terraform, Cloud Formation).
• Experience with Kubernetes Operators, Helm, Git Ops (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.

• Cloud certifications (AWS, Azure, or GCP – e.g., Solutions Architect, Dev Ops Engineer).
• Experience with agentic AI frameworks (CrewAI, Lang Graph, Auto Gen).
• Background in hybrid or…