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About This Role
About the Job:
The Red Hat Services team is looking for a Consultant to implement practical AI solutions that directly solve client challenges using Red Hat OpenShift and Red Hat AI. In this role, you are hands-on throughout technical deployments on engagements that produce tangible outcomes—such as cost-optimized inference, model services with integrated chargeback, or operationalized AI agents. You will work closely with customer teams to turn defined goals and delivery dates into fully landed, functional implementations. To help customer platform teams sustain success long after delivery, you will establish reusable assets on every engagement, including reference architectures, runbooks, and harnesses.
You will act as a key advocate for Red Hat AI solutions, combining technical experience with strong interpersonal skills to solve client problems and scale enterprise AI deployments. Ideal candidates combine a strong background in open-source AI frameworks with a solid grasp of business processes.
We seek people excited about working with us and growing their careers at Red Hat. We want to be transparent about what would make you most successful in the role, and if you are excited by reading the job description and feel you are suitable for the part, we encourage you to apply.
This position requires candidates to work on-site with clients throughout specified locations, so a willingness to travel to customer locations for about 30-40 weeks per year throughout North America is required. Successful applicants must reside in a state where Red Hat is registered to do business and hold an Active TS/SCI Clearance.
What you will do:
Red Hat customers aim to personalize AI across three core areas: their models, their data, and their operational AI. Your responsibilities align directly with these key pillars:
- Guide customers in implementing scalable, production-ready AI solutions using Red Hat OpenShift and Red Hat AI.
- Deploy, optimize, and serve AI models (vLLM, KServe, model registries, pipelines) and baseline inference costs using benchmarking tools and recipe-based optimization.
- Implement retrieval and context patterns (RAG, context ingestion, permission-aware context) and evaluate model performance using tools like Eval Hub to ensure release quality.
- Configure and maintain Models-as-a-Service and GPU-as-a-Service capabilities on OpenShift AI, including multi-tenancy, AI gateways, metering, and self-service accelerator access across NVIDIA, AMD, and other hardware.
- Build and integrate agentic systems and automation workflows using Ansible Automation Platform and modern agent frameworks (MCP, A2A, OpenAI-compatible APIs, LangGraph, etc.) with security, RBAC, and audit ability built in.
- Continuously learn, grow, and adapt to new skills, AI frameworks, and technologies while sharing knowledge, runbooks, and recipes back to internal communities of practice.
- Travel weekly, Monday-Thursday, to client locations.
What you will bring:
- Required Experience: + Experience with delivering an implementation based on an architectural design. + Demonstrated ability to collaborate on project timelines and maintaining project and technical documentation. + Experience working collaboratively across diverse teams and stakeholders.
- Technical Skills: + Current OpenShift expertise: cluster architecture, GitOps, multi-cluster management, security and observability, GPU-enabled nodes. + Experience with Red Hat AI or its upstreams: OpenShift AI, vLLM, model serving with KServe, model registries and pipelines, and at least one of llm-d, LLM Compressor or Eval Hub. + Working knowledge of how enterprises run generative AI today: inference economics, model selection and optimization, RAG and evaluation, agent frameworks and harnesses, MCP, and the governance and cost pressures that shape production decisions. + Application development fluency on Kubernetes: containers, APIs, CI/CD and the trusted software supply chain (SBOM, signing, policy gates), in at least one of Python, Go or Java. + Hands-on experience with Ansible Automation Platform for enterprise automation, including event-driven workflows, or the capability to rapidly transition skills from comparable tools. + Experience delivering hands-on technical implementations based on architectural designs in enterprise environments. + Ability to meet stakeholder needs and carry-out expectations regarding project timelines, technical deliverables, and scope of work. + Experience working collaboratively across diverse technical teams, platform engineers, and customer stakeholders. + Knowledge and desire to mentor team members and customer engineers to foster self-sufficiency.
After joining Red Hat, here's how your skill set will evolve and what you'll learn during your first year in the role. An intensive customized enablement program on Red Hat AI technologies and Services solutions will layer on AI specifics.
In two months, be prepared to join a project team by acquiring the following:
- Understanding of how to build production-ready technical solutions integrated with existing enterprise systems.
- Learn technologies and consulting skills to enhance your abilities through enablements designed and taught by Red Hat experts and certification.
- Through everyday networking and community events, gain exposure and collaboration within Red Hat Services & the more prominent organization.
Within six months, be ready to support customer success on a project by attaining the following:
- Successful, collaborative delivery of customer projects as a technical lead through expertise in Red Hat’s technologies.
- Knowledge of how a customer use case can be developed into a project plan and how those requirements align with Red Hat’s technologies.
- Continue expanding your knowledge and network, both internal and external, through enablement, communities, customers, and meetups.
Within 12 months, demonstrate technical leadership by accomplishing the following:
- Successfully implementing complex, large-scale solutions in customer environments.
- Active engagement and knowledge sharing with our internal and external communities of practice on lessons learned, best practices, and how-tos.
The salary range for this position is $96,440.00 - $154,190.00. Actual offer will be based on your qualifications.Pay Transparency
Red Hat determines compensation based on several factors including but not limited to job location, experience, applicable skills and training, external market value, and internal pay equity. Annual salary is one component of Red Hat’s compensation package. This position may also be eligible for bonus, commission, and/or equity. For positions with Remote-US locations, the actual salary range for the position may differ based on location but will be commensurate with job duties and relevant work experience.
About Red Hat
Red Hat is the world’s leading provider of enterprise open source software solutions, using a community-powered approach to deliver high-performing Linux, cloud, container, and Kubernetes technologies. Spread across 40+ countries, our associates work flexibly across work environments, from in-office, to office-flex, to fully remote, depending on the requirements of their role. Red Hatters are encouraged to bring their best ideas, no matter their title or tenure. We're a leader in open source because of our open and inclusive environment. We hire creative, passionate people ready to contribute their ideas, help solve complex problems, and make an impact.
Benefits
- Comprehensive medical, dental, and vision coverage
- Flexible Spending Account - healthcare and dependent care
- Health Savings Account - high deductible medical plan
- Retirement 401(k) with employer match
- Paid time off and holidays
- Paid parental leave plans for all new parents
- Leave benefits including disability, paid family medical leave, and paid military leave
- Additional benefits including employee stock purchase plan, family planning reimbursement, tuition reimbursement, transportation expense account, employee assistance program, and more!
Note: These benefits are only applicable to full time, permanent associates at Red Hat located in the United States.
Inclusion at Red Hat
Red Hat’s culture is built on the open source principles of transparency, collaboration, and inclusion, where the best ideas can come from anywhere and anyone. When this is realized, it empowers people from different backgrounds, perspectives, and experiences to come together to share ideas, challenge the status quo, and drive innovation. Our aspiration is that everyone experiences this culture with equal opportunity and access, and that all voices are not only heard but also celebrated. We hope you will join our celebration, and we welcome and encourage applicants from all the beautiful dimensions that compose our global village.
Equal Opportunity Policy (EEO)
Red Hat is proud to be an equal opportunity workplace and an affirmative action employer. We review applications for employment without regard to their race, color, religion, sex, sexual orientation, gender identity, national origin, ancestry, citizenship, age, veteran status, genetic information, physical or mental disability, medical condition, marital status, or any other basis prohibited by law.
Red Hat does not seek or accept unsolicited resumes or CVs from recruitment agencies. We are not responsible for, and will not pay, any fees, commissions, or any other payment related to unsolicited resumes or CVs except as required in a written contract between Red Hat and the recruitment agency or party requesting payment of a fee.
Red Hat supports individuals with disabilities and provides reasonable accommodations to job applicants. If you need assistance completing our online job application, email [email protected]. General inquiries, such as those regarding the status of a job application, will not receive a reply.
Salary Context
This $96K-$154K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 2239 roles with salary data).
View full AI/ML Engineer salary data →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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Red Hat, 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 $212,040 based on 13,780 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($125K) sits 41% below the category median. Disclosed range: $96K to $154K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Red Hat AI Hiring
Red Hat has 6 open AI roles right now. They're hiring across AI/ML Engineer. Positions span TX, US, VA, US, Riyadh, Riyadh, Saudi Arabia. Compensation range: $154K - $215K.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 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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