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Brian Himanek

Computer Vision Engineer
Worked with Amazon and Deloitte.
Experienced leader in digital transformation and customer success, driving revenue growth and operational efficiency.

Michael Schroter

Computer Vision Engineer
Expert in data insights & visualizations, predictive analytics, & computer vision.
Michael Schroter is a data and business analyst who specializes in providing business analytics solutions, including data insights/descriptive analytics, data visualizations, text mining, interactive dashboards/business intelligence, predictive analytics, text analytics, and computer vision. He is highly proficient in tools such as Python Data Stack, AWS, Cython, Pytorch, Python SpaCy, RStudio, MySQL, OracleSQL, Arcpy, Geopanda, Linux (Ubuntu, Debian), SAS Enterprise Miner, Tableau, and more.

Peter Faasse

Computer Vision Engineer
Former data science manager for Career Analytics.
As a data scientist, Peter Faase has expertise in solving complicated data problems and working with Big Data to generate useful insights, intelligence, and applications. Combining his strong communication, presentation, and analytical skills, he has created data-driven solutions for organizations by designing and running complex data analyses trajectories using machine learning, predictive analyses, Artificial Intelligence (AI), and other statistical and mathematical techniques. He is skilled with tools such as Python, MySQL, SPSS, and Excel and also familiar with Agile Scrum techniques.

Brian Wu

Computer Vision Engineer
Former senior engineer at NCS Singapore.
Brian Wu is an R&D engineer with 11+ years of experience designing and deploying computer vision and deep learning algorithms on embedded platforms such as FPGA and ARM. He specializes in FPGA/ZYNQ SoC design using Verilog HDL and GPU acceleration with CUDA and OpenGL. His expertise spans SLAM/Visual SLAM, ROS, VR/AR, stereo vision, 3D sensing, object detection, and motor control. He is proficient in C/C++, Python, JavaScript, Golang, and Verilog, and experienced with TensorFlow and Darknet. He also builds full-stack systems using Django, React/Vue/Next.js, MySQL, Node.js, and Go.

Manu Jeevan Prakash

Computer Vision Engineer
Specializes in Python, machine learning & data science.
Manu Prakash is an analytics and digital marketing expert with over four years of experience in SEO, content marketing and SEM, and a documented record of success in increasing online presence and brand awareness. He has worked at the intersection of business and data science at KDnuggets, Crayon Data, Redevon IT Technologies, and Ideas2IT. As a writer, he blogs about big data, Python, data science, and machine learning. Manu is also Google Analytics, Google AdWords, and HubSpot Content Marketing certified.
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Frequently asked questions

Most teams see matched candidates within 48 hours. Our pre-vetted network means we are not starting from scratch when you reach out. If we do not have the right person available, we will tell you upfront rather than scramble.
Practitioners vet practitioners. Your growth marketer candidate gets interviewed by someone who has run growth at similar companies, not a recruiter checking keywords. We combine technical assessments, portfolio reviews, and cultural fit screening before anyone reaches your inbox.
Contract talent works on defined projects with clear deliverables. Embedded talent integrates into your team for ongoing work, attending standups and using your tools. Both can transition to full-time if the fit is right. We help you choose based on scope, timeline, and budget.
If it is not working in the first few weeks, we will find you a replacement at no additional cost. We are not in the business of defending bad matches. Most clients never need this because we invest heavily in fit upfront.
We offer flexible engagement models: hourly for short-term projects, monthly retainers for ongoing work, or placement fees for full-time hires. No hidden fees, no long-term contracts unless you want them. Your account manager will walk through options on your discovery call.
Yes. Someone who thrived at Google with dedicated QA, DevOps, and product managers may flounder when they have to wear all three hats. We look for people who have operated at your stage, pace, and level of ambiguity.
Yes, and not just the flashy demo-building part. Our Agent Operations practice includes people who have run AI agents in production and dealt with the messy reality of keeping them reliable, cost-effective, and safe.
Most marketplaces optimize for volume. They send you 10 candidates hoping one sticks. We optimize for fit. We would rather say “we do not have the right person” than waste your time. Our 95% satisfaction rate comes from being selective, not fast.