Site Reliability Engineer (5757)

SMX•Hollywood, MD

2d•$96,400 - $160,600•Remote

About The Position

SMX is seeking a Mid-Level Site Reliability Engineer to architect, automate, and operate high-performance cloud infrastructure for modern AI/ML platforms. The role bridges MLOps and DevSecOps, deploying scalable cloud-based architectures for compute-intensive AI workloads across commercial, private, hybrid, and government cloud environments. It is fully remote and open to candidates across the United States. In addition to hands-on platform engineering, the engineer will act as a customer-facing Technical Subject Matter Expert, working with stakeholders to translate mission challenges into actionable architectures, demonstrate platform capabilities, and support technical scoping during service engagements. The role includes participation in an on-call rotation and close collaboration with delivery, account, engineering, and security teams. The proposed salary range is $96,400–$160,600 USD. SMX offers health insurance, paid leave, retirement, and learning and development opportunities. Selected applicants may be subject to a background investigation and/or education verification. SMX does not provide immigration or employment authorization sponsorship for this position.

Requirements

3 to 5 years of professional experience in Site Reliability Engineering, DevOps, or Platform Engineering roles.
Ability to communicate complex infrastructure concepts directly to customer stakeholders, act as a technical SME, and support solution demonstrations or technical scoping discussions.
Strong operational proficiency with Kubernetes, including container networking, RBAC, Pod Security, and secrets management.
Practical experience designing, deploying, and managing infrastructure on major cloud platforms such as AWS, Microsoft Azure, or Google Cloud, including networking, identity and access management, encryption, security controls, and logging.
Experience using Terraform or equivalent Infrastructure as Code tooling and Git-based workflows.
Proficiency in Python, Bash, or Go for operational automation and custom tooling development.
Strong troubleshooting skills across compute, storage, networking, and application layers.
Familiarity with security baselines such as CIS benchmarks, NIST, or FedRAMP concepts.

Nice To Haves

Hands-on experience provisioning and operating infrastructure for AI/ML workloads.
Experience operating in regulated or government cloud environments, including AWS GovCloud, Azure Government, or environments subject to FedRAMP and DoD compliance frameworks.
Experience supporting hybrid cloud, multi-cloud, or private cloud infrastructure architectures.
Observability and telemetry experience using tools such as Prometheus, Grafana, Splunk, or Datadog.
Relevant cloud certifications from AWS, Microsoft Azure, Google Cloud, or recognized cloud-native technology providers.

Responsibilities

Architect, deploy, and maintain highly available Kubernetes clusters optimized for AI/ML training, inference, and GPU utilization across cloud environments.
Serve as a technical SME on client engagements, consulting with customer stakeholders to assess requirements, present technical solutions, and explain the value of SMX managed platforms and services.
Partner with delivery and account teams during technical discussions and solutioning to scope, validate, and demonstrate AI/ML and DevSecOps architectures.
Automate infrastructure using Terraform and GitOps patterns, embedding security controls, least-privilege access, and compliance guardrails.
Build and maintain resilient CI/CD pipelines with automated container vulnerability scans, artifact tracking, and audit logging.
Monitor platform performance, capacity, and telemetry; participate in an on-call rotation; and conduct root-cause analysis and operational post-mortems.
Collaborate with engineering and security teams to enforce network segmentation, data isolation, encryption, and key management across high-throughput data pipelines.
Produce client-ready technical documentation, reference architectures, runbooks, and diagrams to support operational excellence and customer handoffs.