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About This Role
Company Description
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
We are seeking a Senior Solutions Architect with deep security expertise to join the Voyager team in the Mirantis Office of the CTO. You will own the security solutioning of k0rdent AI, our platform for building and operating GPU clouds and AI factories, from metal to model.
You will define how k0rdent AI environments are secured end to end: the hardware and firmware they boot from, the hosts, virtual machines and Kubernetes clusters they run, the networks and storage they share, the secrets and certificates that hold them together, and the AI models and applications they serve. You will turn that design into published reference architectures, working code and proofs of concept that run on real hardware with our customers and partners.
As part of the Office of the CTO, research is a core part of the job. You will explore threats, designs and technologies before customers ask for them, prototype ideas that may not ship, and challenge established practice when a better approach exists. Your work will shape where k0rdent AI goes next, not only how it is deployed today.
This role combines research with hands-on, outward-facing work. You will split your time between exploring new designs, writing architecture, building and validating it in the lab, and explaining it to engineers, security teams, executives and conference audiences. You will work with Solutions Architects, Partner Management and Engineering colleagues across many countries and time zones.
Key Responsibilities
Reference architecture
- Design and publish security reference architectures and solution designs for k0rdent AI, covering single-tenant private AI clouds, multi-tenant GPU clouds and hybrid deployments that span public cloud and on-premises infrastructure.
- Define a defence-in-depth model across the stack, from hardware root of trust and firmware to hosts, virtual machines, Kubernetes, networks, storage, models and applications.
- Define tenant isolation boundaries and their guarantees, and document where each boundary is strong, where it is weak, and what it costs to strengthen.
- Map designs to the controls and frameworks customers must meet, and document trade-offs (risk, cost, performance, operability) with defensible reasoning a customer can follow.
Platform and infrastructure security
- Bare-metal and host security: secure and measured boot, TPM-based attestation, BMC and Redfish hardening, firmware integrity, and hardened Linux hosts.
- Virtual machine security: hypervisor and KubeVirt hardening, VM isolation, and the security implications of PCI passthrough and SR-IOV for GPUs and NICs.
- Kubernetes security: RBAC, Pod Security Standards, admission control and policy-as-code, runtime security, and secure multi-cluster and multi-tenant operation.
- Secrets management: secret stores, KMS and HSM integration, envelope encryption, secret delivery to workloads, and rotation.
- Certificates and PKI: internal certificate authorities, automated issuance and rotation, mTLS between services, and workload identity.
- Network security: segmentation and zero-trust designs, Kubernetes network policy, tenant isolation on front-end and GPU fabrics, and DPU-based security enforcement.
- Storage security: encryption at rest and in transit, key management, and tenant isolation on shared block, file and object storage used for training data and model weights.
- Public cloud security: secure landing zones, identity and access management, and network controls on major public clouds, and how they extend to hybrid deployments.
AI model and application security
- Define how Transformer-based models are protected through their lifecycle: provenance and signing of model weights, integrity of training and fine-tuning data, and secure model registries.
- Secure inference services and AI applications against threats such as prompt injection, data and model exfiltration, model theft and abuse of agentic tool access.
- Design isolation for shared GPU infrastructure (MIG, vGPU, passthrough) and assess confidential computing options for protecting models and data in use.
- Secure the software supply chain for platforms, containers and models: image signing, SBOMs, vulnerability management and provenance.
Research and exploration
- Track and evaluate emerging security technologies, standards and threats, such as confidential computing on GPUs, remote attestation, post-quantum cryptography, model signing and AI agent security.
- Prototype alternative designs in the lab, including ones customers have not yet asked for, and measure them against current practice.
- Challenge established or customer-preferred designs when evidence points to a better option, and make the case with data.
- Publish findings as internal research notes and design proposals and, where appropriate, as external papers, blog posts or talks.
Code and proofs of concept
- Build and run proofs of concept with customers and partners, on Mirantis lab hardware and on customer sites, and report results against agreed success criteria.
- Write automation, policies and tooling (Python, Go, Bash, Ansible, Helm, Kubernetes manifests, Terraform, policy-as-code) that make the reference architectures reproducible.
- Assess designs and deployments through threat modelling, configuration review and hands-on testing, and turn findings into design guidance.
Customers, partners and community
- Act as the security subject matter expert in customer discovery, design reviews and architecture workshops, bringing thought leadership and new ideas rather than only reflecting current practice.
- Engage with customer security, risk and compliance teams, and support security questionnaires and architecture reviews for strategic opportunities.
- Work with hardware, software and security partners (e.g. NVIDIA, server OEMs, security vendors) on joint designs and validations.
- Innovate and feed research results back to Product, Engineering and the Mirantis security team, and help shape the k0rdent AI roadmap.
- Present at industry events, webinars and partner summits, and run hands-on workshops for technical audiences.
- Write technical content: reference architecture documents, solution briefs, blog posts and internal enablement.
Location, travel and working model
This role is remote and based in the United States, with a strong preference for the East Coast.
- You will work with a team spread across many time zones; some meetings will fall outside standard local hours.
- Travel of up to 25% for customer engagements, partner meetings, lab work and industry events, primarily within the US and EU.
- Mirantis is an equal opportunity employer. Compensation and benefits are set according to the local market and employment arrangement.
Qualifications
Education and experience
- Bachelor's degree in Computer Science, Computer Engineering, Cybersecurity or a related field, or equivalent practical experience.
- 8+ years in security engineering or security architecture, with at least 3 years securing cloud, Kubernetes or large-scale infrastructure platforms.
- Customer-facing experience as a solutions architect, pre-sales engineer, consultant or technical lead.
Required technical skills
- Cloud security: designing and operating secure deployments on at least one major public cloud (AWS, Azure or GCP) and on private cloud or bare-metal infrastructure.
- Host and virtualisation security: Linux hardening (e.g. SELinux or AppArmor, CIS benchmarks), secure boot, TPM, and hypervisor and VM isolation, including KubeVirt.
- Kubernetes security: RBAC, Pod Security Standards, admission controllers and policy engines (e.g. Kyverno, OPA Gatekeeper), runtime security (e.g. Falco), and multi-tenancy patterns.
- Secrets and key management: tools such as HashiCorp Vault or OpenBao, External Secrets Operator, cloud KMS and HSMs.
- PKI and identity: certificate lifecycle with tools such as cert-manager, mTLS, OIDC and SSO, and workload identity (e.g. SPIFFE/SPIRE).
- Network security: segmentation, firewalling, zero-trust architectures and Kubernetes network policy (e.g. Cilium, Calico).
- Storage and data security: encryption at rest and in transit, key hierarchy design, and access control on shared storage.
- AI security: a working understanding of how Transformer-based models are trained, packaged and served, and the threats specific to them (e.g. OWASP Top 10 for LLM Applications, MITRE ATLAS).
- Software supply chain security: image signing (e.g. Sigstore/cosign), SBOMs, vulnerability scanning and SLSA concepts.
- Programming or scripting in at least one language (Python or Go preferred), and comfortable using Git, CI and infrastructure-as-code.
How you work
We hire for these behaviours as much as for technical depth, and will ask for concrete examples of each.
- Curious. You dig into how attacks actually work, read the specs, advisories and source, and keep up with how AI and infrastructure threats are changing. You question established designs, including the ones customers are used to, when there is a better way.
- Ownership. You take a reference architecture or a POC from the first sketch to a published, validated result, and you stand behind it.
- Proactive. You spot risks and gaps in the product, the documentation or a customer design before you are asked, and you act on them.
- Fast learner. You get productive quickly in unfamiliar hardware, software or customer environments.
- Business acumen. You frame security decisions in terms of risk, cost, compliance and time-to-value, and explain them in those terms to decision makers.
Communication and collaboration
- Excellent written and spoken English; you write documents others can build from.
- Comfortable presenting to large audiences at conferences and events, and running hands-on workshops.
- Able to adapt the message to the audience, from engineers to CISOs and customer executives.
- Experience working in an international, distributed company, across time zones and different work cultures, with Solutions Architects, Partner Management and Engineering teams.
Nice to have
- Experience with compliance frameworks and benchmarks relevant to cloud and AI infrastructure, such as NIST SP 800-53, NIST AI RMF, FedRAMP, SOC 2, ISO/IEC 27001, CIS benchmarks or DISA STIGs.
- Hands-on experience with confidential computing (e.g. AMD SEV-SNP, Intel TDX, NVIDIA GPU confidential computing) and remote attestation.
- Experience securing GPU cloud, neocloud or HPC environments, or NVIDIA Cloud Partner (NCP) style deployments.
- Experience with DPUs (e.g. NVIDIA BlueField) for infrastructure isolation and security offload.
- Offensive security or red-teaming experience, including against AI systems.
- Experience with Mirantis products, MKE, k0s, k0rdent security.
- Security certifications such as CISSP, CCSP, CKS, OSCP, or public cloud security specialty certifications.
- Contributions to open-source security or Kubernetes projects, or a track record of public talks and technical writing.
- Participation in standards or industry bodies (e.g. CNCF TAG Security, OpenSSF, Confidential Computing Consortium, CoSAI), or published research, CVEs, patents or white papers.
- Additional languages beyond English.
Additional Information Why you’ll love Mirantis
- Build the observability foundation for the AI cloud era, working directly with leading GPU cloud operators, NeoClouds, sovereign clouds, and AI-first enterprises
- Collaborate with a world-class, distributed team committed to openness and technical excellence
- Shape the product narrative and influence go-to-market success
It is understood that Mirantis, Inc. may use automated decision-making technology (ADMT) for specific employment-related decisions. Opting out of ADMT use is requested for decisions about evaluation and review connected with the specific employment decision for the position applied for. You also have the right to appeal any decisions made by ADMT by sending your request to [email protected]
By submitting your resume, you consent to the processing and storage of your personal data in accordance with applicable data protection laws, for the purposes of considering your application for current and future job opportunities.
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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. Senior-level AI roles across all categories have a median of $229,300.
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.
Remote Work Context
Remote AI roles pay a median of $188,000 across 2,316 positions. About 6% 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 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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