Senior MLOps Engineer | Remote AI Platform

Jobot; Atlanta (GA); On-site; USD 150,000 - 175,000; Full time

Benefits offered by this job

Work from home (US)

Competitive base salary and equity

Medical/dental/vision insurance

Life and disability insurance

11 paid holidays

Flexible time off

401(k) with 4% match

Home office stipend

Wellness stipend

Job summary

Jobot is seeking a seasoned DevOps/Platform Engineer to deploy, operate, and scale an Azure-based platform used across government-grade SaaS. You will own infrastructure end-to-end, enforce cost discipline, and ensure security with FedRAMP-conscious controls.

The role emphasizes production-grade reliability and cross-team collaboration. You will work in a remote-first environment (US-based) with strong automation, IaC practices, and a focus on scalable, observable AI infrastructure.

Qualifications

• 5+ years in DevOps, platform engineering, or site reliability engineering in SaaS environments.
• Deep Azure experience: AKS, networking, identity (Entra), and monitoring; you have run production Kubernetes.
• Infrastructure as code as your default (Terraform, Bicep, or similar), plus strong scripting; you automate before you document.
• MLOps experience: deploying and operating LLM or ML systems in production, including model gateways, inference infrastructure, or AI observability stacks.
• Demonstrated cost work: you can point to cloud spend you found, explained, and reduced.
• Experience in compliance-heavy environments (FedRAMP, StateRAMP, SOC 2, or similar) is a strong plus.
• Comfortable holding production access, with the discipline that implies.

Responsibilities

• Deploy and operate our Azure platform: AKS, networking, identity, storage, and environments from development through production
• Own infrastructure as code end to end: environments are reproducible, drift is detected, and nothing reaches an environment without platform visibility
• Operate the AI infrastructure layer: self-hosted observability and evaluation tooling (Langfuse), product telemetry, model gateway and per-workload routing, and compliant GovCloud inference paths
• Own cloud and AI cost: metering, budgets, unit economics, MACC drawdown strategy, and active remediation; cost is an engineering metric here, not a finance afterthought
• Harden production access and controls: least privilege, secrets management, audit evidence, and a FedRAMP-conscious security posture
• Partner with AI Operations on the deploy-and-release path: Octopus Deploy, environment promotion, progressive rollout, and rollback
• Build platform reliability: monitoring, alerting, incident response, and capacity planning
• Give the microservices decomposition the platform primitives it needs: service infrastructure, scaling patterns, and clean environment boundaries

Skills

DevOps

Platform engineering

Site reliability

Azure

Entra

Infrastructure as Code

Terraform

Bicep

Scripting

MLOps

Cost optimization

FedRAMP/SOC2 compliance

Production access discipline

Tools

AKS

Kubernetes

Langfuse

Terraform

Bicep

Job description

Jobot is seeking a seasoned DevOps/Platform Engineer to deploy, operate, and scale an Azure-based platform used across government-grade SaaS. You will own infrastructure end-to-end, enforce cost discipline, and ensure security with FedRAMP-conscious controls.

The role emphasizes production-grade reliability and cross-team collaboration. You will work in a remote-first environment (US-based) with strong automation, IaC practices, and a focus on scalable, observable AI infrastructure.