Site Reliability Engineer III

Site Reliability Engineer III

Location: Onsite in Mount Laurel, NJ

Duration: Through 12/31/2026, extensions likely

We are seeking a Site Reliability Engineer (SRE) to support cloud infrastructure, automation, reliability, security, and observability initiatives for AI/ML platform environments. This role is ideal for a mid-to-senior-level engineer with strong cloud and platform engineering experience who can drive scalability, reliability, and automation within Kubernetes-based production environments.

Responsibilities:

• Support Site Reliability Engineering initiatives across AI/ML platform environments.
• Deploy, maintain, and optimize cloud infrastructure across AWS and GCP.
• Build, manage, and maintain Infrastructure as Code (IaC) solutions using Terraform.
• Improve platform reliability, scalability, security, and operational efficiency.
• Administer and support Kubernetes and Amazon EKS environments.
• Monitor and troubleshoot system performance using observability and monitoring tools including Prometheus, Grafana, Datadog, and Elasticsearch.
• Automate operational processes, workflows, and routine administrative tasks using Python and related tooling.
• Support and enhance CI/CD pipelines and deployment automation.
• Troubleshoot complex distributed systems and production issues in highly available environments.
• Collaborate with engineering teams to improve platform performance, monitoring, and operational resiliency.
• Work with technologies including Kubernetes, Docker, AWS, GCP, EKS, Terraform, Prometheus, Grafana, Datadog, Elasticsearch, MySQL, Kafka, and Python.

Qualifications:

Qualifications:

• 4–8 years of experience in Site Reliability Engineering, DevOps, Platform Engineering, Cloud Engineering, or related disciplines.
• Strong hands-on experience with AWS cloud services.
• Hands-on Kubernetes administration and support experience.
• Expertise with Terraform and Infrastructure as Code (IaC) practices.
• Experience designing, supporting, and improving CI/CD pipelines.
• Strong observability and monitoring experience, particularly with Prometheus.
• Experience with Grafana, Datadog, Elasticsearch, or similar monitoring platforms.
• Python programming and scripting proficiency.
• Experience supporting distributed systems and large-scale, highly available production environments.
• Knowledge of algorithms, data structures, software design principles, and system troubleshooting.
• Experience with Docker containers and cloud-native platforms.

Preferred Qualifications:

• Bachelor's degree in Computer Science or a related technical discipline.
• Experience supporting AI/ML platforms or infrastructure.

Compensation

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