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About This Role
Company Description
Mirantis is the Kubernetes\-native AI infrastructure company, enabling organizations to build and operate scalable, secure, and sovereign infrastructure for modern AI, machine learning, and data\-intensive applications. By combining open source innovation with deep expertise in Kubernetes orchestration, Mirantis empowers platform engineering teams to deliver composable, production\-ready developer platforms across any environment—on\-premises, in the cloud, at the edge, or in sovereign data centers. As enterprises navigate the growing complexity of AI\-driven workloads, Mirantis delivers the automation, GPU orchestration, and policy\-driven control needed to manage infrastructure with confidence and agility. Committed to open standards and freedom from lock\-in, Mirantis ensures that customers retain full control of their infrastructure strategy. https://www.mirantis.com/
Job Description
Our organization is establishing an Americas\-based AI Infrastructure \& Platform Operations unit dedicated to the management of expansive AI ecosystems utilizing NVIDIA GPU acceleration, high\-speed interconnects, Kubernetes, and bleeding\-edge platform frameworks.
This team maintains the reliability, efficiency, and architectural integrity of vital AI service platforms across a global datacenter footprint. Positioned at the nexus of core infrastructure and network engineering, you will sustain the high\-performance environments essential for contemporary AI application suites.
This position offers the chance to engage with pioneering AI hardware while driving the development of automated operational capabilities via the k0rdent AI platform.
Responsibilities
- Monitor, operate, and support production AI infrastructure platforms.
- Investigate and resolve infrastructure, networking, hardware, and platform\-related incidents.
- Support NVIDIA GPU infrastructure and associated platform services.
- Monitor and troubleshoot Kubernetes\-based environments.
- Investigate performance, availability, and reliability issues across infrastructure and platform components.
- Collaborate with engineering teams, hardware vendors, Data Center personnel, and service delivery teams to resolve technical issues.
- Participate in incident response, root cause analysis, and operational improvement activities.
- Contribute to improvements in monitoring, observability, automation, and operational processes.
- Maintain operational documentation, runbooks, and knowledge articles.
Qualifications Required Experience
- 3\+ years of experience in infrastructure operations, platform operations, network operations, site reliability engineering, cloud operations, datacenter operations, or related technical roles.
- Strong Linux administration and troubleshooting skills.
- Good understanding of networking concepts and experience diagnosing infrastructure\-related issues.
- Working knowledge of Kubernetes in production environments.
- Experience supporting production infrastructure and services.
- Strong analytical and problem\-solving skills.
- Experience working within structured operational and incident management processes.
- Excellent communication and collaboration skills.
Ability to work within a shift\-based operational environment.
Preferred Experience
Experience in one or more of the following areas is highly desirable:
- NVIDIA GPU infrastructure and accelerated computing platforms.
- InfiniBand networking and NVIDIA UFM.
- Kubernetes platform operations.
- AI infrastructure or HPC environments.
- Site Reliability Engineering (SRE) or Platform Engineering.
- Observability platforms such as Grafana, Prometheus, ELK, or OpenTelemetry.
- Infrastructure automation technologies and Infrastructure\-as\-Code practices.
- Large\-scale distributed systems and production platforms.
Why Join Us?
- Work with some of the most advanced AI infrastructure environments in production today.
- Gain exposure to NVIDIA GPU technologies, Kubernetes platforms, and high\-performance networking environments.
- Help define how next\-generation AI infrastructure is operated and supported.
- Be part of a team shaping the future of AI\-powered operations through k0rdent AI.
- Join a growing organisation investing heavily in AI infrastructure and platform services.
Additional Information What does Mirantis offer you?
- Work with an established Silicon Valley leader in the cloud infrastructure industry;
- Work with exceptionally passionate, talented and engaging colleagues, helping Fortune 500 and Global 2000 customers implement next\-generation cloud technologies;
- Be a part of cutting\-edge, open\-source innovation;
- Thrive in the high\-energy environment of a young company where openness, collaboration, risk\-taking, and continuous growth are valued;
- Professional development and training;
- Attend conferences and working groups;
- Company outings, happy hours, hackathons, and tech talks;
- Receive a competitive compensation package with a strong benefits plan.
We are a Leader for Container Management in G2 (\#2 after AWS)!
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Mirantis, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Mirantis AI Hiring
Mirantis has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% of all AI roles offer remote work.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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