Remote Mlops Engineer Jobs
Remote Mlops Engineer jobs are open across the U.S. in software, cloud infrastructure, and AI-driven industries, at remote-first companies and distributed engineering teams ranging from early-career to staff-level roles. Employers hiring remotely right now include TetraScience, oura, and Kohl's. See the openings below and apply to the ones that match your experience.
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Role Overview:
You will own the Azure platform behind SimpliGov’s AI-native delivery model—infrastructure, Kubernetes, networking, observability, AI serving, and cost discipline. This is hands-on production engineering within a FedRAMP-conscious environment, where security, auditability, and reliability are core responsibilities.
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.
What We Offer:
- 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/disabilities insurances
- 11 Paid holidays
- Flexible time off
- 401k plan with 4% employer match
- Monthly stipends for wellness and home office expenses
Legal Disclaimers:
SimpliGov does not sponsor applicants for work visas.
The US base salary range for this full-time position begins at $150,000.00 + bonus + benefits. Individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status.
SimpliGov participates in the federal government's E-Verify program, which confirms employment authorization of all newly hired employees and most existing employees through an electronic database maintained by the Social Security Administration and Department of Homeland Security. For new hires, the E-Verify process is completed in conjunction with the Form I-9 Employment Eligibility Verification on or before the first day of work. It is not used as a screening tool.
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Find Remote Mlops Engineer JobsRemote Mlops Engineer Job Market
Who's Hiring



Top Industries Hiring
- Retail
- Media & Entertainment
What Employers Look For
The qualifications that appear most often in remote mlops engineer jobs.
- Proficiency in Python and experience building and maintaining ML pipelines at scale
- Hands-on experience with containerization and orchestration tools such as Docker and Kubernetes
- Familiarity with at least one major cloud platform including AWS, GCP, or Azure ML services
- Experience with ML experiment tracking and model registry tools such as MLflow or Weights and Biases
- Understanding of CI/CD principles and tooling applied to machine learning model deployment workflows
- Bachelor's degree in computer science, data engineering, or a closely related technical field
Tips for Your Remote Mlops Engineer Job Search
Apply early to remote roles that fit
Migrate Mate lists remote mlops engineer openings from across the U.S. in one place so you can find roles that match your stack and apply directly. Remote postings at well-known distributed teams fill fast, so applying in the first few days after a listing goes live improves your odds noticeably.
Build a public pipeline portfolio
Remote mlops employers want proof you can ship without being watched. Publish reproducible pipelines on GitHub covering model training, versioning with tools like DVC or MLflow, and deployment to a cloud provider. A working example beats a bullet point every time.
Signal async communication skills explicitly
Remote mlops teams run on written communication across time zones. Mention in your application how you document architecture decisions, write post-mortems, or maintain runbooks. Employers screening for remote readiness treat clear written output as a technical signal, not a soft skill.
Prepare for remote-first technical interviews
Remote mlops interviews frequently include live coding in a shared environment, infrastructure design walkthroughs over video, and questions about on-call practices for distributed systems. Practice narrating your reasoning out loud while working through container orchestration or monitoring scenarios so your thought process is visible to the interviewer.
Remote Mlops Engineer Jobs: Frequently Asked Questions
How do I get a remote mlops engineer job?
Focus on companies that already run distributed engineering teams, since they have established workflows for remote mlops work and hire for it consistently. Remote employers screen heavily for self-direction, clear async written communication, and hands-on experience with tools like Kubernetes, MLflow, and cloud platforms such as AWS or GCP. A public portfolio of deployed pipelines or documented model monitoring setups gives your application a concrete edge over a resume alone.
Which companies hire remote mlops engineers?
Companies hiring remote mlops engineers right now include TetraScience, oura, and Kohl's, based on current remote listings on Migrate Mate as of August 2026. Remote-first software firms, cloud infrastructure companies, and AI product teams are the most consistent sources of remote mlops engineer openings because their entire engineering culture is built around distributed collaboration.
Can you get a remote mlops engineer job with no experience?
Yes, but remote entry-level mlops roles are harder to land because employers expect you to work independently from day one with minimal hand-holding. Smaller remote-first startups and contract-to-hire teams are the most realistic entry points. What opens the door is demonstrable work: a GitHub repository showing CI/CD pipelines, containerized model deployments, or experiment-tracking setups signals readiness even without a formal job title behind it.
Do you need a degree for remote mlops engineer jobs?
Not always. Many remote employers care more about what you can build and operate than where you studied. Demonstrated proficiency with orchestration tools, model registries, and cloud infrastructure carries real weight in hiring decisions. That said, roles at larger enterprises or those requiring security clearance may still list a degree as a requirement, so it depends on the employer and seniority level.
Which industries hire the most remote mlops engineers?
The sectors hiring the most remote mlops engineers are Retail and Media & Entertainment, based on current remote listings on Migrate Mate as of August 2026. These sectors hire mlops engineers remotely because their data science and model deployment workflows are already built on cloud infrastructure that distributed teams can operate from anywhere.
See All 10 Remote Mlops Engineer Jobs
Find roles that match your experience and apply in just a few clicks.
Find Remote Mlops Engineer Jobs