Lead Cloud Infrastructure Engineer / Site Reliability Engineer
Role summary
The Lead Cloud Infrastructure Engineer / Site Reliability Engineer will be responsible for ensuring the stability, performance, and security of a Federal region's cloud platform, with a strong focus on maintaining a FedRAMP-compliant environment. This role involves managing infrastructure and operations using an "everything as code" approach, leveraging automation and best practices for efficiency and scalability. Key responsibilities include designing, deploying, and scaling AI/ML/LLM infrastructure, managing Kubernetes environments, automating data and model pipelines, and implementing robust monitoring and observability. The position requires participation in on-call rotations, leading incident response, and providing technical leadership for infrastructure initiatives.
### Who you are
- 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
- 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
- Willingness to undergo a Single Scope Background Investigation, if required
- The successful candidate must be a U.S. citizen and may need to perform work that the U.S. government has specified can only be carried out by a U.S. citizen on U.S. soil
### What the job involves
- 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 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