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About This Role
About Us
SUSE is a global leader of enterprise open source software. By transforming community innovations into secure, sovereign and AI-ready solutions, SUSE empowers customers to escape vendor lock-in and regain control of their IT destiny. Through industry-leading Linux, Kubernetes, Edge and AI infrastructure solutions, SUSE delivers the flexibility to innovate everywhere—from the data center to multi-cloud and out to the edge. Only SUSE also manages many Linux and Kubernetes distributions. At SUSE, Choice Happens because we prioritize community, interoperability and relentless innovation. Discover how we power mission-critical resilience at www.suse.com .
AI Domain Solution Architect
Job Description
We are seeking an AI-focused Domain Solution Architect (DSA) to join our North America team. This role requires deep technical expertise in AI infrastructure, accelerated computing, Kubernetes, and enterprise AI platforms, combined with the ability to translate that expertise into solutions for complex customer environments.
As a Domain Solution Architect, you will be a technical specialist embedded within the sales organization, working alongside Account Executives, Solution Architects, partners, and customers to architect and validate enterprise AI solutions.
You will focus on helping customers move AI workloads from experimentation into secure, scalable, production-ready environments. The ideal candidate combines strong Kubernetes, Linux, and infrastructure architecture skills with hands-on knowledge of GPU-accelerated computing and the NVIDIA AI ecosystem.
Key Responsibilities
- Drive Technical Architecture & Solution Design: Lead discovery sessions, technical evaluations, architecture workshops, and infrastructure sizing for complex customer AI environments—translating enterprise workloads such as LLM inference, fine-tuning, and RAG into scalable architectures built on SUSE AI Factory, Rancher Prime, and the NVIDIA accelerated computing stack.
- Deliver Technical Validation & Field Sales Execution: Partner with Account Executives and field Solution Architects throughout the sales cycle to lead technical qualification, build targeted PoCs, deliver hands-on demonstrations, and develop technical proposals and RFI/RFP responses.
- Guide Agentic AI Architecture: Help customers understand the infrastructure, integration, security, and governance requirements for agentic AI, multi-agent systems, and Model Context Protocol (MCP) integrations in enterprise production environments.
- Collaborate Across the AI Ecosystem: Jointly architect customer solutions with strategic ecosystem partners—including hardware vendors, systems integrators, and cloud providers—to optimize AI compute, high-speed networking, storage, and overall platform architecture.
- Build Field Capability & Repeatable Assets: Develop reusable reference architectures, solution blueprints, demonstrations, and technical enablement materials that scale AI expertise across regional and global field teams.
- Act as the Voice of the Customer: Capture technical feedback and emerging platform requirements from customer environments and communicate them to SUSE Product Management and Engineering to help inform product direction.
- Serve as an AI Infrastructure SME: Translate complex topics—including GPU architecture and partitioning, workload scheduling, multi-tenancy, high-performance networking, storage, and air-gapped operations—into clear technical and business value for audiences ranging from platform engineers to C-suite technology executives.
Skills & Experience
- Presales Architecture Experience: Proven track record translating deep technical expertise into effective customer solutions, proposals, presentations, demonstrations, RFP responses, and hands-on workshops in a presales, solution architecture, or technical consulting role.
- Technical Communication & Leadership: Ability to simplify complex AI and infrastructure concepts for both technical and executive audiences, connecting technical architecture to business outcomes. Demonstrated ability to coordinate cross-functional teams, influence stakeholders in matrixed environments, and drive execution with a team-first mindset.
- AI Infrastructure & Accelerated Computing: Hands-on experience designing, operating, and sizing GPU-accelerated infrastructure for AI training, fine-tuning, and inference workloads. Practical understanding of GPU memory and compute characteristics, resource partitioning, workload scheduling, utilization, and multi-tenancy, together with familiarity with technologies such as vLLM, NIM, RAG, and vector databases.
- NVIDIA Ecosystem Proficiency: Working knowledge of the NVIDIA AI and accelerated computing ecosystem, including GPU architecture, NVIDIA AI Enterprise, CUDA, GPU and Network Operators, NCCL, GPUDirect, MIG, Run:ai, and high-performance interconnects.
- Container Management & Cloud-Native Platforms: Expert-level Kubernetes architecture and cluster administration experience, with RKE2, K3s, and Rancher Prime preferred. Deep proficiency with Helm, GitOps, CNI, container runtimes, observability, workload scheduling, and multi-cluster lifecycle operations.
- Linux Systems Expertise: Strong Linux systems knowledge, including networking, storage, containers, security, troubleshooting, and performance analysis in production environments.
- SUSE Platform Knowledge: Familiarity with, or the ability to rapidly acquire expertise in, SUSE AI Factory, SUSE Rancher Prime, SUSE Linux Enterprise Server (SLES), SUSE Security (NeuVector), and SUSE Observability.
- High-Performance Networking & Storage: Solid understanding of high-bandwidth, low-latency networking—including Ethernet, RoCEv2, RDMA, and InfiniBand—and distributed and object storage architectures supporting high-performance AI workloads.
- Enterprise Security & Operations: Experience designing secure production architectures, including container security, software supply-chain security, identity and access management, workload isolation, air-gapped/disconnected environments, and enterprise AI governance requirements.
Success in This Role
Success will be measured by your ability to bring deep AI infrastructure expertise into strategic customer opportunities, accelerate technical validation, enable field sales teams, develop repeatable AI solution patterns, and establish SUSE as a trusted platform for production enterprise AI.
This position is subject to a background check(s), including criminal, credit, and/or employment references. The candidate is required to complete the background check(s) once an offer has been accepted. This will be conducted by SUSE’s external provider, where legally permitted.
For US Only - US Pay Transparency Disclaimer
If this role is filled in the United States of America, the starting base salary will be within the applicable pay range displayed below. In addition to base salary, this position may be eligible for a commission plan and a competitive benefits package. US benefits include comprehensive medical coverage, life and disability insurance, a 401(k) plan, an Employee Assistance Program, and generous paid time off and leave policies.
Final compensation will be determined based on factors such as experience, skills, geographic location, internal equity, and budget. This pay transparency information applies to US-based roles only.
Base Pay Range Min & Max
99,000
329,000
Job
Sales What We Offer
We empower you to be bold, driving your career to create the future you want. We celebrate and reward your achievements.
SUSE is a dynamic environment that is evolving rapidly, thus requiring agility, strong entrepreneurship and an open mind.
This is a compelling opportunity for the right person to join us as we continue to scale and prosper.
If you’re a big thinker, obsessed by execution and thrive in a dynamic environment in which you can tangibly create a lasting legacy, !
We give you the freedom to be yourself. You will work in a global community of unique individuals – like you – with different backgrounds, talents, skills and perspectives. A truly open community where everyone is welcome, has a voice and is encouraged to reach their full potential regardless of age, gender, race, nationality, disability, sexual orientation, religion, or any other characteristics.
Sounds like the right fit for you? . A recruiter will contact you if your skills match our current or any future positions. In the meantime, stay updated on the latest SUSE news and job vacancies by joining our Talent Community .
SUSE Values
SUSE's culture is centered on four key values - Choice, Community, Trust, and Innovation - which are deeply integrated with our open source ethos. SUSE fosters a diverse and inclusive environment where our people are encouraged to be themselves.
Choice:
We are continuously making choice happen
We are accountable for our choices
We never get complacent
Community:
Nobody is smarter than everybody
We embrace diversity of thought
We are “open source first, upstream first” where collaboration benefits all
Trust:
We are trusted to deliver with integrity
We offer trust by default, and do not wait for it to be earned
We foster an environment where everyone trusts each other
Innovation:
We foster a culture of experimentation, and embrace change by challenging the norm
We are committed to continuous improvement, creativity and adaptability
Ideas are great, but without execution they are just ideas
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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At SUSE Software Solutions Germany GmbH, 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 $212,000 based on 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
SUSE Software Solutions Germany GmbH AI Hiring
SUSE Software Solutions Germany GmbH has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in MA, US.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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