Senior MLOps Engineer

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

Benefits offered by this job

100% work from home (US based only)

Own platform ownership and cost focus

Competitive base, bonus, and equity

Medical, dental, and vision insurance

Disability insurance

11 Paid holidays

Flexible time off

401k plan with 4% employer match

Monthly stipend for home office

Monthly wellness stipend

Job summary

Jobot is seeking an experienced DevOps/Platform Engineer to own the Azure-based platform and its cost discipline for a government-focused AI SaaS. You will deploy, operate, and scale the platform across AKS, networking, identity, and observability while ensuring least-privilege security and auditability.

You will collaborate with AI Ops, own the infrastructure as code lifecycle, and drive production readiness with strong emphasis on compliance, governance, and cost controls.

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 engineering

MLOps

Cost optimization

FedRAMP compliance

Kubernetes

Azure/AKS

Infrastructure as code

SaaS

Tools

Terraform

Bicep

Langfuse

Octopus Deploy

GovCloud / Azure Gov

Job description

Job details

Own where changes land and what it costs to run

This Jobot Job is hosted by: Charles Simmons

Salary: $150,000 - $175,000 per year

A bit about us

Small, mid staged AI native SaaS startup helps state and local governments modernize paper-based processes into intelligent, AI-driven digital workflows. As we evolve into an AI-first platform, our development velocity, model iteration frequency, and cross-team complexity increase. A reliable, cost-disciplined platform is essential to scale safely and predictably.

Why join us

• 100% work from home (US based only)
• Own the platform that build, validation, and release loops run on and deploy to: infrastructure, environments, Kubernetes, Networking, observability, and the AI serving and routing layer.
• Competitive base, bonus, and equity options
• Medical, dental, and vision insurance plans, with significant employer contributions for employees AND dependents (contributions based on base-level plan; buyup plans available at additional costs)
• Company-sponsored life, short-term, and long-term disability insurance
• 11 Paid holidays
• Flexible time off
• 401k plan with 4% employer match
• Monthly stipend for home office expenses
• Monthly wellness stipend

Job Details

Everything runs on Azure, and the platform is getting more interesting: an AI product suite heading toward general availability, self-hosted AI observability and telemetry inside a FedRAMP-conscious boundary, autonomous agents participating in delivery, and a microservices decomposition in flight. You will deploy, operate, and scale that platform, and you will own its cost discipline.

This is a production seat with production access, and we treat that as an engineering responsibility, not a badge: least privilege, audit trails, and environment integrity are part of the job, because our customers are governments.

You will work alongside our AI Operations Engineer, who owns the agentic delivery system (the loops that build, validate, and release code). You own the platform those loops run on and deploy to: infrastructure, environments, Kubernetes, networking, observability, and the AI serving and routing layer. The boundary is simple: they own how changes move; you own where changes land and what it costs to run.

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

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

Key Competencies

• Treats environment integrity as sacred: no invisible changes, no snowflake servers, no heroics that cannot be audited
• Cost literacy: reads a cloud bill the way an engineer reads a stack trace
• Automates first: your instinct is a pipeline or a policy, not a runbook step
• Think in the open: surfaces risk early and documents what you build
• Calm in production incidents; rigorous in the postmortem

What This Role Is NOT

This is not a ticket-queue operations role and not a NOC seat. If your model of DevOps is executing change requests that other people design, this is not the fit. It is also not a research MLOps role: the AI infrastructure here serves a shipping product for government customers, with the reliability and compliance expectations that implies.

How We Work

We run an AI-native product development lifecycle. Autonomous agents participate in planning, coding, validation, and release; humans own judgment, standards, and direction. Work moves through a Plan-and-Review cadence rather than ceremony-heavy Agile. Two standards are non-negotiable: you own and can explain everything you ship, no matter what produced it, and you think in the open, surfacing uncertainty early rather than burying it.

Equal Opportunity Employer

Jobot is an Equal Opportunity Employer. We provide an inclusive work environment that celebrates diversity and all qualified candidates receive consideration for employment without regard to race, color, sex, sexual orientation, gender identity, religion, national origin, age (40 and over), disability, military status, genetic information or any other basis protected by applicable federal, state, or local laws. Jobot also prohibits harassment of applicants or employees based on any of these protected categories. It is Jobot’s policy to comply with all applicable federal, state and local laws respecting consideration of unemployment status in making hiring decisions.

Background Check Policy

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