Lead Cloud Infrastructure Engineer / Site Reliability Engineer (SRE)

Responsibilities

• 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 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.
• 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

Qualifications/Requirements

• Bachelor’s or Master’s degree in Computer Science, Engineering, or related field, or equivalent experience.
• 8+ years in SRE, DevOps, 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 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.

Preferred Qualifications

• Cloud certifications (AWS, Azure, or GCP – e.g., Solutions Architect, DevOps Engineer).
• Experience with agentic AI frameworks (CrewAI, LangGraph, AutoGen).
• 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.

Additional Requirements

• U.S. citizenship at the time of hire.
• Residence within the contiguous United States.
• Willingness to undergo a Single Scope Background Investigation, if required.