World Wide Technology is actively hiring for 31 AI and machine learning positions, concentrated in AI/ML Engineer (29) and AI Product Manager (2) roles. Posted salary ranges span $104K - $300K, with 90% of listings disclosing compensation. The median posted ceiling sits at $190K. Hiring spans Remote, US, Hartford, CT, US, St. Louis, MO, US, with 45% of roles available remotely. The most frequently requested skills across these postings are Azure, Aws, Gcp, Anthropic, Gemini. Mid-level roles account for 51% of openings.
Skills & Technologies
Locations
Remote, US, Hartford, CT, US, St. Louis, MO, US, San Francisco, CA, US, Phoenix, AZ, US
Hiring by Role Category
Open Positions (showing 25 of 31)
Lead Cloud Solution Architect- Cloud AI & Data
AI Solutions Director- East Region
Enterprise AI Adoption Lead
Solutions Managing Director, AI Tech Strategy & Consulting
Sr Consultant, Enterprise AI Adoption
Security Solutions Principal - AI Security
Sr. Infrastructure AI Automation Consultant
AI Security Engineer
Vice President - AI Transformation Center
AI Solutions Architect - Global Financial Services
Senior Product Manager, AI
AI Solutions Architect - East Region
AI Business Development Manager
Principal Architect, AI-Native Security
Technical Solutions Architect - AI Storage & Data Platforms
AI Sales Leader - Global Service Provider
AI Services Consultant - Global Financial Services
AI Services Consultant - West Region
AI Solutions Executive - Global Financial Services
AI Solutions Executive - West Region
AI Solutions Director - West Region
AI Solutions Director- Central Region
Associate Client Solutions Director- Digital & AI Platforms
AI Solutions Executive - Central Region
What World Wide Technology's hiring tells you
31 open AI roles across 2 role types puts this company in the scaling phase: past the initial proof of concept, building out a real team. Expect more structure than a startup but less bureaucracy than a major. Good fit for engineers who want ownership without building from zero. Posted compensation range ($104K - $300K) suggests transparent and competitive pay practices.
The skill mix here leans toward Azure in AI/ML Engineer roles. That is a clue about what World Wide Technology is building: teams hire for the work in front of them, not the work they wish they were doing.
Questions worth asking in the World Wide Technology interview loop
The signals above come from public job postings. The signals you actually need come from the conversation. A few questions calibrated to this company's tier:
- What problem did the first AI hire solve, and how has scope grown since?
- Where does AI sit in the engineering org, and who owns the budget?
- What is the on-call expectation for AI systems? (If unclear, that means it has not happened yet.)
World Wide Technology AI and ML Hiring
World Wide Technology has 31 active AI and ML roles in our dataset. Open positions span AI/ML Engineer, AI Product Manager. Compensation ranges from $104K - $300K across disclosed roles. Roles are based in Remote, US, Hartford, CT, US, St. Louis, MO, US, San Francisco, CA, US.
Salary Benchmarks
The market median for AI roles is $217,500. AI/ML Engineer roles pay a median of $218,750 across the market. AI Product Manager roles pay a median of $216,175 across the market. Top-quartile AI compensation starts at $272,100.
Skills World Wide Technology Looks For
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.
AI Role Categories
AI/ML Engineer
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.
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.
Market compensation for AI/ML Engineer roles: $218,750 median across 3,817 positions with disclosed pay.
AI Product Manager
AI Product Managers define what AI features get built and why. They translate business problems into ML-solvable tasks, work with engineering to scope model requirements, and own the metrics that determine if an AI feature is working. The role requires a rare combination of technical fluency and product instinct.
Technical fluency with ML concepts is essential, though you won't be writing models. Expect to understand training data, evaluation metrics, model limitations, and responsible AI practices. SQL and basic Python are increasingly expected. Experience with A/B testing, data analysis, and product analytics is baseline. Understanding LLM capabilities and limitations is now a core requirement.
Market compensation for AI Product Manager roles: $216,175 median across 270 positions with disclosed pay.
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.
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.
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.
Frequently Asked Questions
Frequently Asked Questions
Related Resources
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