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About This Role
5\+ years of experience in enterprise storage and data platform solutions, with demonstrated ability to both assess technical fit and advise customers on AI data readiness strategy, including:
Storage \& Data Infrastructure
- Deep working knowledge of high\-performance parallel file systems
- Strong familiarity with enterprise NAS and object storage platforms
- Ability to evaluate storage platform fit against AI workload requirements, including training, checkpointing, and inference, without necessarily performing the hardware sizing or implementation
NVIDIA AI Data Platform
- Working knowledge of NVIDIA AI Data Platform design requirements, reference architectures, and validation frameworks
- Understanding of GPU\-Direct Storage, NVAIE, and Mission Control as capabilities, with the ability to articulate their value and applicability to customer AI use cases
- Familiarity with how AIDP\-validated storage platforms integrate with NVIDIA compute architectures including DGX, HGX, MGX, and OVX
Data Pipelines \& Frameworks
- Working knowledge of data pipeline patterns and orchestration tools including Apache Spark, Ray, Dask, and Apache Airflow, sufficient to advise on design fit and architectural trade\-offs
- Understanding of data lakehouse and data fabric concepts including Delta Lake, Apache Iceberg, Apache Hudi, and Unity Catalog
- Familiarity with data ingestion and movement tools such as Apache Kafka, Apache NiFi, and Airbyte
Professional Skills
- Demonstrated ability to engage executive and technical audiences with equal fluency
- Experience delivering advisory workshops, architecture presentations, and strategic recommendations to enterprise customers
- Strong written communication skills; capable of producing thought leadership content, briefings, and reference architectures
- Sound organizational skills and ability to manage multiple concurrent customer engagements
Nice to Have
- Python scripting for data pipeline automation or storage performance testing
- Experience with NVIDIA Run:ai, Slurm, or other workload schedulers as they relate to storage I/O optimization
- Previous exposure to NVIDIA Enterprise and NCP reference architectures
- Experience with cloud storage integration: AWS S3, Azure Blob, Google Cloud Storage, and hybrid data fabric designs
- Familiarity with data governance, data cataloging, and compliance frameworks in AI environments
Certain states and localities require employers to post a reasonable estimate of salary range. A reasonable estimate of the current base pay range for this position is $125,000\.00 to $156,000\.00 annually. Actual salary will be based on a variety of factors, including shift, location, experience, skill set, performance, licensure and certification, and business needs. The range for this position in other geographic locations may differ. Certain positions may also be eligible for variable incentive compensation, such as bonuses or commissions, that is not included in the base pay.
The well\-being of WWT employees is essential. So, when it comes to our benefits package, WWT has one of the best. We offer the following benefits to all full\-time employees:
- Health and Wellbeing: Health, Dental, and Vision Care, Onsite Health Centers, Employee Assistance Program, Wellness program
- Financial Benefits: Competitive pay, Profit Sharing, 401k Plan with Company Matching, Life and Disability Insurance, Tuition Reimbursement
- Paid Time Off: PTO and Sick Leave (starting at 20 days per year) \& Holidays (10 per year), Parental Leave, Military Leave, Bereavement
- Additional Perks: Nursing Mothers Benefits, Voluntary Legal, Pet Insurance, Employee Discount Program
We strive to create an environment where all employees are empowered to succeed based on their skills, performance, and dedication. Our goal is to cultivate a culture of belonging that encourages innovation, collaboration, and respect for all team members, ensuring that WWT remains a great place to work for All!
If you have any questions or concerns about this posting, please email taposting@wwt.com.
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Requirements:
Why WWT?
At World Wide Technology, we work together to make a new world happen. Our important work benefits our clients and partners as much as it does our people and communities across the globe. WWT is dedicated to achieving its mission of creating a profitable growth company that is also a Great Place to Work for All. We achieve this through our world\-class culture, generous benefits and by delivering cutting\-edge technology solutions for our clients.
Founded in 1990, WWT is a global technology solutions provider leading the AI and Digital Revolution. WWT combines the power of strategy, execution and partnership to accelerate digital transformational outcomes for organizations around the globe. Through its Advanced Technology Center, a collaborative ecosystem of the world's most advanced hardware and software solutions, WWT helps clients and partners conceptualize, test and validate innovative technology solutions for the best business outcomes and then deploys them at scale through its global warehousing, distribution and integration capabilities.
With over 12,000 employees across WWT and Softchoice and more than 60 locations around the world, WWT's culture, built on a set of core values and established leadership philosophies, has been recognized 14 years in a row by Fortune and Great Place to Work® for its unique blend of determination, innovation and creating a great place to work for all.
Want to work with highly motivated individuals on high\-performance teams? Join WWT today!
What will you be doing?
World Wide Technology is seeking a Technical Solutions Architect (TSA) – AI Storage \& Data Platforms to join our AI \& High\-Performance Architectures (HPA) practice within the Global Solutions \& Architecture (GS\&A) team. This is a strategic, customer\-facing role focused on architecting and delivering high\-performance storage, data platform, and data pipeline solutions that power AI and machine learning workloads at scale.
You will serve as WWT's subject matter expert on AI data infrastructure, spanning the full stack from raw storage and data movement to data frameworks, lakehouse architectures, and NVIDIA AI Data Platform design, working alongside our world\-class ecosystem partner Everpure.
Why Join the AI \& Data Team?
Our AI \& Data Team sits at the intersection of infrastructure and intelligence, designing the data foundations that make AI possible. You'll work with the most advanced storage, data platform, and AI technologies on the market, alongside the partners shaping the modern AI and data ecosystem. As part of WWT's Global Solutions \& Architecture organization, the team turns emerging technology into production\-ready solutions and uses the Advanced Technology Center to prove out architectures before they reach a customer environment. This is a high\-visibility role with direct impact on WWT's AI go\-to\-market strategy and our customers' most ambitious AI initiatives.
What You'll Own
AI Data Platform Architecture Lead the design and sizing of end\-to\-end AI data infrastructure, from GPU\-Direct Storage and high\-performance parallel file systems to data lakehouse and data fabric architectures, aligned to NVIDIA AI Data Platform design requirements and partner reference architectures.
Partner Solution Development Serve as WWT's technical lead for our storage and data platform partner ecosystem, working closely with our strategic ecosystem partner Everpure. Develop and maintain joint solution designs, reference architectures, and go\-to\-market plays.
Pre\-Sales \& Customer Engagement Engage directly with customers to assess AI data infrastructure requirements, define architectures, and deliver compelling solution proposals — including HLDs, BOMs, and LLDs — for enterprise and hyperscale AI workloads.
Field Enablement \& Thought Leadership Develop and deliver training, briefings, workshops, and reference architectures that enable WWT's field teams and partners to position and sell AI storage and data platform solutions with confidence.
Pipeline \& Business Development Collaborate with regional architects, sales leadership, and the broader GS\&A practice to identify, qualify, and advance new business opportunities in the AI data infrastructure space.
Practice \& Partner Alignment Maintain OEM and partner certifications, track product roadmaps, and align WWT's go\-to\-market strategy with partner initiatives, including NVIDIA AI Data Platform certifications and the Everpure partner program.
Responsibilities
- Advise customers on AI Data Readiness strategy, assessing current data infrastructure, pipeline maturity, and platform capabilities against the requirements of their targeted AI use cases
- Translate NVIDIA AI Data Platform features, including GPU\-Direct Storage, NVAIE, and Mission Control, into tangible business and workload outcomes for enterprise customers and field audiences
- Develop and deliver AI Data Readiness workshops, assessments, and frameworks that help customers identify gaps between their current data environment and the requirements of production AI
- Guide customers on how AIDP\-validated platforms such as VAST, Weka, Dell PowerScale, and NetApp ONTAP AI enable specific AI use cases across training, inference, and MLOps workflows
- Partner with HPA and infrastructure architects to provide advisory context on how data strategy, pipeline design, and platform selection affect AI Factory outcomes at the customer level
- Develop use case guidance on how data pipeline and framework design choices, including orchestration, transformation, and ingestion patterns, affect AI workload performance and data readiness
- Create enablement content, including briefings, workshops, reference architectures, and thought leadership, that equips WWT field teams to confidently position AIDP value in customer conversations
- Support field teams with deal\-level advisory, translating AIDP platform capabilities into business justification and customer outcomes
- Maintain current knowledge of AIDP roadmap, certifications, and partner program requirements to ensure WWT's advisory position reflects the latest capabilities
- Participate in AI and Data Team meetings, partner briefings, and training to remain aligned across the practice
- Travel as required to customer sites, partner events, and WWT facilities
Travel Requirements: 25–50%
Salary Context
This $125K-$156K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At World Wide Technology, 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. This role's midpoint ($140K) sits 36% below the category median. Disclosed range: $125K to $156K.
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.
World Wide Technology AI Hiring
World Wide Technology has 31 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager. Positions span Remote, US, Hartford, CT, US, St. Louis, MO, US. Compensation range: $104K - $300K.
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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