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About This Role
Mirantis, an IREN company, 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 About the Role & Mission
AI infrastructure is undergoing a generational shift. Disaggregated, raw GPU hardware must be transformed into multi-tenant, production-ready, and sovereign AI clouds - without locking enterprises into a single hyperscaler or hardware vendor. That is where k0rdent AI by Mirantis comes in.
k0rdent AI serves as the declarative "super control plane" that manages complex, heterogeneous GPU infrastructure from Metal-to-Model™. We are hiring a Head of Solutions Architecture to own the technical win, build our pre-sales organization from the ground up, and lead the architecture strategy on high-value, long-cycle compute deals.
This is not a demo-jockey role . You will be an executive technical partner to NeoClouds, AI-native startups, enterprise AI teams, research labs, and sovereign buyers. You will lead high-stakes architecture debates on interconnect topologies, build TCO models that challenge hyperscaler economics, construct reusable pre-sales machinery, and shape our core product roadmap directly from field intelligence.
What You’ll Own
- Build & Scale the Pre-Sales Engine (40%)
+ Hire, coach, and retain a world-class team of Solutions Architects and Field Engineers; establish the global pre-sales operating model as headcount and deal volume scale. + Build reusable, scalable field assets: discovery frameworks, reference architectures, TCO/benchmark models, POV playbooks, and RFP response libraries. + Set a high technical quality bar across the organization; run enablement so every SA can speak credibly to GPU architecture, high-speed fabrics, and container orchestration.
- Own the Technical Win in Strategic Deals (40%)
+ Partner with Account Executives as the technical executive on strategic enterprise opportunities with long sales cycles (6–18+ months) and multi-million-dollar ACV/TCV. + Execute structured qualification (MEDDPICC or equivalent) to map decision criteria, surface economic buyers, and build win plans around technical champions. + Architect end-to-end solutions across compute, networking, storage, and orchestration; deliver sizing, capacity plans, and TCO comparisons vs. public clouds and self-built alternatives. + Design and drive POVs/POCs: define strict success criteria upfront, run performance benchmarks, and convert results into commercial momentum.
- Serve as the Technical Voice of the Customer (20%)
+ Feed structured product, capacity, and feature requirements directly back to product management, engineering, and supply planning teams. + Partner closely with the NVIDIA field ecosystem (Cloud Partner program, reference architectures, joint pursuits) to win strategic accounts. + Influence roadmap prioritization, packaging, and GTM strategy based on real-world field feedback.
By joining us, you will lead the technical field engine behind k0rdent AI , shaping how the world's most innovative companies build and operate GPU-powered infrastructure at scale.
Qualifications What We’re Looking For
- Enterprise Deal Leadership: Proven track record leading technical sales in complex B2B environments, ideally with multi-year committed-capacity or reserved-capacity deal structures.
- Player-Coach Leadership: Track record of hiring, developing, and leading Solutions Architecture or Field Engineering teams—with the technical depth to lead the room on hard deals while scaling others to do the same.
- Hands-on AI/ML Infrastructure Experience: You have stood up or operated production ML workloads (distributed training and multi-node inference) and deeply understand performance bottlenecks across interconnect, memory bandwidth, I/O, and cluster scheduling.
- NVIDIA Stack & High-Speed Fabric Mastery: Fluency across Hopper and Blackwell architectures (H100/H200, GB200 NVL72, B200) and reference systems (DGX, HGX, MGX). Deep understanding of NVLink/NVSwitch domains, InfiniBand, Spectrum-X Ethernet, RDMA/RoCE, and DPUs.
Strongly Preferred:
- Pre-sales or infrastructure leadership experience at a NeoCloud, hyperscaler AI organization, or accelerated-hardware vendor.
- Hands-on experience with cloud-native orchestration for AI: Kubernetes (GPU operators, device plugins), KubeVirt, Metal3/Ironic bare-metal provisioning, and Slurm.
- Familiarity with high-throughput parallel/object storage systems for AI pipelines and data center physical constraints (power, cooling, rack density).
Additional Information 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 $211,950 based on 13,048 positions with disclosed compensation.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Mirantis AI Hiring
Mirantis has 3 open AI roles right now. They're hiring across AI/ML Engineer. Based in Boston, MA, US.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 median).
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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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