Technical Solutions Architect I

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 distribution capabilities.
Softchoice, a World Wide Technology company, supports commercial and SMB markets in the U.Want to work with highly motivated individuals on high-performance teams? The Computer Vision Specialist is a member of WWT’s Global Solutions & Architecture team, part of AI & Data Solutions, focused on computer vision and the machine learning behind it. Customer demand is growing across manufacturing, warehousing and distribution, energy and utilities, venues and live events, and healthcare. We are looking for someone who brings deep subject matter expertise in computer vision and computer vision machine learning and applies it effectively in sales situations: helping account teams qualify opportunities, working with customers to define the problem and how success will be measured, developing a high level design, and validating it in the WWT ATC and AI Proving Ground. This is a net new position. It reports to the AI & Data Solutions Practice Manager, has no direct reports, and works alongside the Physical AI sub team, the data science and edge AI specialists, sellers, and WWT’s computer vision partners. Computer vision solutioning. Apply computer vision expertise to customer opportunities across the modern toolkit: classical computer vision, deep learning, video intelligence, spatial understanding, and vision language models. Translate a business problem such as safety, quality, throughput, or asset monitoring into a defined use case, the cameras and sensors it needs, the data required, the model approach, the integrations, and the metrics that will judge success.
Computer vision machine learning. Guide model selection and approach for tasks such as classification, detection, segmentation, tracking, and pose estimation, including when to use pretrained or foundation models, fine tune, or train custom models. Advise on dataset curation, annotation, and synthetic data, and on how models will be evaluated, optimized for edge deployment, monitored for drift, and retrained over time.
Work with customers to gather technical requirements and develop a high level design that addresses their desired outcome.
Design, build, and run proofs of concept and demonstrations in the ATC and AI Proving Ground, including work on edge hardware. Connect the solution to measurable outcomes such as cost, quality, throughput, and risk.
Evaluate and work with WWT’s computer vision partners and ISVs, and NVIDIA and Intel technologies, so recommendations are neutral and defensible.
Enablement and content. Share your expertise with sellers and solution architects so more of the team can carry computer vision conversations, and contribute research articles, demos, and collateral that build WWT’s presence in the market.
Bachelor’s degree in Computer Science, Engineering, Robotics, or a related field. Advanced degrees or relevant AI, machine learning, or computer vision certifications preferred.
Work Experience: Substantial hands on experience designing, training, and deploying computer vision models for real business problems, plus customer facing work in consulting, presales, or solution architecture. Candidates should be able to describe solutions they personally built, including the cameras and sensors, data sources and labeling approach, model choices and training approach, integrations, failure scenarios, deployment approach, and the metrics used to measure success.
Computer vision and computer vision machine learning. Depth across classical methods, deep learning, and vision language models, with judgment about when each is the right tool. Fluency in metrics such as precision, recall, mean average precision, and latency, and in error analysis that explains why a model fails in a particular environment.
Data and model lifecycle. Data collection, annotation, synthetic data, training, validation, deployment, monitoring for drift, and retraining.
Video intelligence. Detecting events, activities, and anomalies across live and recorded video.
Edge and deployment. Running and optimizing vision workloads on edge hardware such as NVIDIA Jetson, using techniques such as quantization and TensorRT, and understanding the tradeoffs among edge, on premises, and cloud inference.
Customer engagement. Commercial aw