Interested in this AI/ML Engineer role at World Wide Technology?
Apply Now →Skills & Technologies
About This Role
Qualifications
------------------
- 8\+ years of experience in cloud data architecture on one or more major cloud platforms (AWS, Azure, GCP, Databricks, Snowflake, or Microsoft Fabric), giving you the judgment to distinguish a sound technical concept from a flawed one
- Demonstrated ability to quickly move a solution from idea to sellable offering, including building the sales enablement content needed to support it
- Demonstrated experience transforming and migrating data from legacy and source systems into modern cloud data platforms, including the patterns, tooling, and pitfalls involved
- Demonstrated ability to partner with technical architects as a peer to work through the best approach and tooling for a complex or ambiguous client problem
- Experience in a consulting, systems integrator, or professional services environment, packaging and selling technical offerings to enterprise clients
- Bachelor's degree in Computer Science, Information Systems, Engineering, Business, or equivalent practical experience
- Excellent executive communication skills, equally credible presenting business strategy to leadership and technical rationale to engineers
Preferred Skills
--------------------
- Prior experience advising C level executives (CIO, CDO, CTO) on cloud data or AI strategy
- Relevant certifications across AWS, Azure, GCP, Databricks, or Snowflake
- Familiarity with low/no code data tooling such as Apache NiFi, Informatica, and AWS Glue
- Personal experience using agentic AI tools in day to day technical work, such as AI coding assistants and agentic workflow tooling
- Experience partnering with business development or alliance teams on hyperscaler or ISV co investment programs
Technical Depth
-------------------
- Data transformation and migration into modern cloud data platforms, including ETL/ELT pipelines, legacy system extraction, and data quality and validation during migration
- Designing and implementing modern data architecture patterns: data lakehouse, data mesh, data fabric, medallion architecture on vendor platforms such as Databricks, Snowflake, Microsoft Fabric or other similar cloud solutions
- AI operationalization across clouds: Bedrock, Azure AI Foundry, Vertex AI, SageMaker, RAG architecture, agentic solution architecture patterns, AI governance
- Streaming and real time architectures: Kafka, Kinesis, Azure Event Hub, Flink
- Data governance, lineage, and cost/FinOps principles as they relate to platform decisions and packaging
What Success Looks Like in Year One
---------------------------------------
- At least one new solution developed under this role has moved from idea to sellable, revenue generating offering, contributing measurably to practice growth
- Sales enablement materials measurably reduce presales cycle time and reduce dependency on this role for individual pursuits
- Partner co investment funding is secured and deployed against enablement or demand generation
- Technical sales support and steering committee involvement have directly influenced at least one marquee win and caught at least one delivery risk early on a strategic engagement
Travel
----------
Up to 25% of the time, domestic and international.
Want to learn more about Consulting Services? Check us out on our platform:
https://www.wwt.com/consulting\-services
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 $140,000 to $175,000 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. 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 (Medical \& Prescription), Dental, and Vision Care, Onsite Health Centers (MO \& IL), Employee Assistance Program, Wellness program
- Financial Benefits: Competitive Pay, Profit Sharing, 401k Plan with Company Matching, Life and Disability Insurance, Flexible Spending Accounts, Tuition Reimbursement
- Paid Time Off: PTO \& Holidays, Parental Leave, Medical Leave, Military Leave, Bereavement, Day of Caring
- Additional Perks: Family Planning Benefits, Nursing Mothers Benefits, Voluntary Legal, Voluntary Supplemental Accident/Illness/Hospital, Voluntary ID Theft, Pet Insurance, Employee Discount Program
Note: This is not an all\-encompassing list and should not be used as a complete description of the plan's benefits. For more information, see our US benefits website at wwt.com/us\-benefits.
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 require accessibility accommodation(s) or adjustment during any stage of the hiring process, please let your WWT Recruiter know. The recruiter will work with you to understand your needs and help ensure an accessible experience throughout the interview process.
World Wide Technology is an Equal Opportunity Employer.
*If you have any questions or concerns about this posting, please email* *[email protected]**.*
\#LI\-MP2
\#LI\-REMOTE
Requirements:
Why WWT?
World Wide Technology (WWT) strives to make a new world happen. WWT's work benefits clients and partners as much as it does its people and community across the globe.
Founded in 1990, WWT brings together strategy, deep technical expertise and world\-class partnerships to help public and private sector organizations design, build and scale intelligent AI, digital, cybersecurity, cloud and infrastructure solutions. Through its Advanced Technology Center (ATC)—a collaborative ecosystem featuring state\-of\-the\-art hardware and software—WWT enables clients and partners to conceptualize, test and validate innovative technology and then deploy solutions at scale using its global integration and distributions capabilities.
With more than 14,000 team members and over 60 locations globally, WWT's culture—grounded in core values and leadership philosophies—has been recognized by Fortune® and Great Place to Work for its commitment to innovation, trust and creating a great place to work for all. WWT provides products and services to large enterprise, global service provider and public sector clients in up to 130 countries across six continents. Softchoice, a World Wide Technology company, supports commercial and SMB markets in the U.S. and Canada.
Want to work with highly motivated individuals on high\-performance teams? Join WWT today!
What is the Solutions Consulting \& Engineering (SC\&E) Team and why join?
Solutions Consulting \& Engineering is an organization that is Customer Focused and Solutions Led. We deliver end\-to\-end and emerging solutions to drive customer satisfaction, increase profitability and growth. Our success is enabled by our world\-class management consulting, delivery excellence and engineering brilliance. Our goal is to bring together business acumen with full\-stack technical know\-how to develop innovative solutions for our clients' most complex challenges.
Position Overview
---------------------
As Lead Cloud Solution Architect, Cloud AI \& Data, you lead WWT's Cloud Data solutions, the platforms and data transformation capability that power client outcomes in analytics and AI.
Your primary mandate is solution development: identifying and shaping new Cloud Data offerings that fuel practice growth, positioning and packaging them for the market, and supporting the sales and delivery teams who bring them to clients. You provide technical sales support on strategic pursuits and sit on the steering committee for strategic engagements.
Success in this role requires real, practitioner level knowledge of cloud data platforms, how data is transformed and migrated into these modern platforms, and how it is then prepared to power analytics and AI outcomes, paired with the business judgment to turn that knowledge into offerings clients will buy and WWT can deliver at scale.
Key Responsibilities
------------------------
### Solution Development \& Practice Growth
- Identify, shape, and bring to market new Cloud Data solutions that fuel practice growth, ahead of and beyond maintaining the existing offering
- Own the Cloud Data offering as a product: roadmap, positioning, and packaging across AWS, Azure, GCP, Databricks, and Snowflake
- Own pricing strategy, SOW/LOE templates, and scope frameworks that let Solution Architects price and structure engagements consistently
- Drive P\&L style accountability for the offering: track adoption, win rates, margin, and growth against target, and adjust the portfolio accordingly
- Partner with Business Development Managers on hyperscaler and ISV co investment programs (AWS, Microsoft, Google Cloud, Databricks, Snowflake), providing the technical and solution input needed to secure funding for enablement, demand generation, and joint pursuits
- Partner with Practice Leadership to prioritize which capabilities to build, retire, or acquire based on client demand and competitive signal
- Collaborate with Cloud Migration, Security, and Infrastructure solution owners to keep the offering integrated across the broader WWT Cloud portfolio
### Technical Sales Support \& Strategic Delivery Oversight
- Serve as the senior technical evaluator on strategic and complex pursuits, assessing whether a proposed concept or architecture is sound before it reaches the client
- Partner with the technical architect to determine the best technical approach, architecture pattern, and toolset for a given strategic pursuit or engagement
- Review and validate SOWs, LOEs, and technical proposals for architectural soundness on strategic pursuits before they go to the client
- Sit on the steering committee for strategic accounts and engagements, providing technical and business oversight at key milestones, not day to day delivery management
- Identify delivery risk early on these engagements and work with delivery leadership to course correct before issues reach the client
### Sales Enablement
- Build and maintain the enablement toolkit (battlecards, demos, discovery guides, objection handling, pricing tools) that lets Solution Architects run pursuits independently, packaging reference architectures and technical assets into sales facing materials
- Train and enable Solution Architects and sellers on the offering so the practice scales without a bottleneck at this role
- Review win/loss patterns across pursuits and feed learnings back into enablement content and packaging
- Support RFP/RFI responses with reusable, prebuilt solution narratives rather than one off authoring
Salary Context
This $140K-$175K 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. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($157K) sits 28% below the category median. Disclosed range: $140K to $175K.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.