Senior MLOps Engineer

Jobot; Sterling (VA); Remote; USD 150,000 - 175,000; Full time

Benefits offered by this job

100% remote (US-based)

Platform ownership

Competitive compensation

Medical/dental/vision insurance

Life, disability insurance

11 Paid holidays

Flexible time off

401k with 4% match

Home office stipend

Wellness stipend

Job summary

Jobot seeks an experienced DevOps/Platform Engineer to own the Azure-based platform, cost discipline, and deployment landings across environments. You will deploy, operate, and scale AKS clusters, networking, identity, and observability for a government-grade SaaS product.

In partnership with the AI Operations Engineer, you will drive IaC pipelines, budgeting, and security controls in a FedRAMP-conscious setup, delivering reliable, auditable deployments to government customers while maintaining

Qualifications

• 5+ years in DevOps, platform engineering, or SRE in SaaS environments.
• Deep Azure experience: AKS, networking, identity, monitoring; production Kubernetes.
• Infrastructure as code: Terraform, Bicep, or similar; strong scripting; automate before documenting.
• MLOps experience: deploying and operating LLM/ML systems in production.
• Cost work: ability to point to cloud spend you found, explained, and reduced.
• Experience in compliance-heavy environments (FedRAMP, StateRAMP, SOC 2 or similar).
• Comfortable holding production access with discipline and auditability.

Responsibilities

• Deploy and operate the Azure platform: AKS, networking, identity, storage, and environments from development through production.
• Own infrastructure as code end-to-end; environments reproducible and drift detected.
• Operate the AI infrastructure: self-hosted observability, model gateway, routing, and GovCloud paths.
• Own cloud and AI cost: metering, budgets, unit economics, and remediation; cost as an engineering metric.
• Harden production access and controls: least privilege, secrets management, audit trails.
• Partner with AI Operations on the deploy-and-release path: Octopus Deploy, environment promotion, rollout, and rollback.
• Build platform reliability: monitoring, alerting, incident response, capacity planning.
• Think in governance: surfaces risk early and documents what you build.

Skills

DevOps

Platform engineering

SRE

Cost optimization

MLOps

FedRAMP compliance

Automation

Tools

Terraform

Bicep

Langfuse

Kubernetes

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
• Thinks 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 Statement

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

Sometimes Jobot is required to perform background checks with your authorization. Jobot will consider qualified candidates with criminal histories in a manner consistent with any applicable federal, state, or local law regarding criminal backgrounds, including but not limited to the Los Angeles Fair Chance Initiative for Hiring and the San Francisco Fair Chance Ordinance.

Privacy Notice

Information collected and processed as part of your Jobot candidate profile, and any job applications, resumes, or other information you choose to submit is subject to Jobot’s Privacy Policy, as well as the Jobot California Worker Privacy Notice and Jobot Notice Regarding Automated Employment Decision Tools which are available at jobot.com/legal.

Communication Consent

By applying for this job, you agree to receive calls, AI-generated calls, text messages, or emails from Jobot, and/or its agents and contracted partners. Frequency varies for text messages. Message and data rates may apply. Carriers are not liable for delayed or undelivered messages. You can reply STOP to cancel and HELP for help. You can access our privacy policy here: jobot.com/privacy-policy