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Find jobsDevelop AI-driven image recognition solutions with expert Computer Vision developers. Optimize real-time analysis—hire now and onboard in no time.
Alejandro is a data scientist working in Python, Databricks, and SQL who builds forecasting and optimization systems. He cut an FMCG planning team from 8 people to 1 and stockouts by 10%, and as a solo founder built a restaurant invoice SaaS where an LLM plus arithmetic checks pushed extraction accuracy from 96% to over 99%.
Arshveer is a Python AI engineer who builds LLM and data systems for regulated workflows. Recently he led a lead discovery platform processing 18M+ records for financial advisors, adding autonomous scraping agents and compliance controls; earlier he built document pipelines that handled 100K+ financial files with 97% accuracy.
Adil is a Python AI engineer who builds production LLM and computer vision systems. He recently architected a legal document agent with source-cited answers for contracts and filings, and built a defense workflow that used parallel agents, confidence scoring, and human review to cut manual processing time by over 60%.
Senior AI Engineer with a background in deep learning and computer vision, now building production LLM and agent systems on AWS and Azure. He has led a team delivering multi-agent code migration, built a WhatsApp booking agent and a customs-declaration platform with a self-learning extraction pipeline. His infrastructure work includes quota admission control and a custom LiteLLM routing strategy across Bedrock regions.
Daniel is an AI/ML engineer working mainly in Python who has shipped LLM, computer vision, and industrial ML systems, from Bedrock-based marketplace content generation at million-user scale to predictive maintenance on sensor data. He now runs his own AI consultancy and builds grounded assistants with RAG, ETL, and source-linked document workflows.
Erich is an applied AI engineer working in Python and OpenAI on production voice agents. He built healthcare and telecom calling systems that handle 15,000 and 5,000+ calls a day, tied them into CRM workflows, and also founded a computer vision product that turned a single product photo into a sellable catalog, reaching 200 users in two months.
Walter is an iOS engineer working in Swift, SwiftUI and Objective-C who builds unusual mobile products, from an AR tattoo preview app with a SwiftUI layer over legacy Objective-C to a football-skills prototype using Vision, CoreML and stereo vision across two iPhones. Earlier he built a hi-res audio player that handled 10,000+ albums.
Alexandre is an AI and ML engineer working in Python and LangGraph who now leads agentic systems for a job platform, shipping about two production use cases per quarter across four business units. Earlier at TikTok, he owned moderation models for direct messages and increased coverage 600× while keeping precision high.
Natalia is a Python AI engineer who builds LLM and computer vision systems, from RAG-backed teaching assistants to GPU-optimized video inference. She led the AI and backend architecture for a 3D learning platform, and earlier built a predictive maintenance system that cut downtime from 45 to 15 minutes across 300+ machines.
Developers who got their wings at
How it works
Fill in a short form
Tell us about your computer vision project, timeline and hours, then look through our vetted developers.
Take a 30-minute call
A Lemon.io matching expert goes over your needs and gives you a budget estimate.
Meet 1–3 candidates
You get 1–3 developer profiles, usually within 24 hours. Interview them the way you normally would.
Start working
Your developer joins your team part-time or full-time and works directly with you.
Reached out on Monday evening, connected Tuesday morning, had four qualified candidates by Wednesday.
We had an excellent experience. Process is fast from the initial intake through setting up payment.
The developers helped us speed up. They quickly learned their part of the app and we’re grateful for their contribution.
We vet every developer
Every computer vision developer passes a live interview with a senior engineer. We accept 1.2% of applicants.
We hand-pick your matches
A matching expert picks people by your stack, domain and time zone, and tells you why each one fits.
We handle the paperwork
We handle contracts, NDAs and invoices. You pay Lemon.io, and we pay your developer.
We stay after you hire
Your Lemon.io manager stays in touch with you both. If it doesn't work out, we find a replacement for free.
The lemon tea
Models that work on a curated dataset often fail on real cameras, lighting and angles. Our computer vision developers build for the messy images your product actually gets.
Data before architecture
Strong candidates ask about labeling quality, class balance and edge cases before picking a model. Chasing benchmark scores alone fails.
Vision profiles, open to you
No other marketplace runs an open database like Lemon's. Filter computer vision profiles by ONNX, MLflow or data annotation work and ask for introductions.
POC sized upfront
Not sure how big the proof of concept is? A Lemon analyst puts hours on it before you hire.
Why hire computer vision developers
Defect detection
Quality checks on production lines that catch what people miss.
Document and ID scanning
OCR and field extraction from photos of real documents.
On-device models
Models optimized with ONNX to run on phones and cameras.
Labeling pipelines
Annotation workflows that keep training data consistent.
The experience with Lemon.io has been fantastic. The interview process has been good, the caliber of people – excellent and integration has been very smooth.
We needed extra developers to clean off all these bugs so the company could skyrocket.
We needed extra AI engineers to keep our ambitious project running.
Because of the niche nature, you will typically need to go beyond general job boards such as Indeed or LinkedIn in order to find Computer Vision developers. Focus on regional communities of AI, ML and Computer Vision. It is also advised to look into research publications as well, going to your industry conferences and trying reaching out the university labs doing computer vision work.
More so, if you are a company that needs computer vision, there are platforms like Lemon.io where businesses can be connected to the best individual developers for hire with experience in Computer Vision, OpenCV and TensorFlow libraries. We are ready to find the perfect Senior developer skilled in Computer Vision within 48 48-hour timelines.
We are big believers in test driving a hire here at Lemon.io, even (and maybe especially!) when it comes to computer vision. To make sure you have a chance to see how a Computer Vision developer would perform on your specific tasks before fully committing to a contract with them, we offer up to 20 hours of paid trial work at our standard rates.
On top of that, like all our roles, we have a zero-risk replacement guarantee should they not work out. But we promise you will not need to take this option – our seasoned team will find you a perfectly matched senior developer.
Yes, there is a high demand for Computer Vision developers. Computer vision expertise is a very sought-after skillset. Because of the significant breakthroughs in AI technology lately, all kinds of companies have realized its usefulness – medical professionals who analyze images and diagnose illnesses, programmers working on self driving cars, augmented reality engineers who blur the digital with the real, and retail stores analyzing buying patterns.
The world has awakened to a technology that’s changing how we process data, automating tasks previously requiring people’s input (literally, the act of seeing something!) and how we analyze events. No matter the field, there is an increasing desire for developers who understand and specialize in Computer Vision and all it makes possible.
We’re dedicated to making your search for the perfect Computer Vision developer efficient. In almost all cases, we can provide a curated shortlist of highly qualified candidates within a rapid turnaround time of 48 hours. You then set the pace for additional onboarding stages. However, we’ve found many companies can onboard their new Computer Vision developer within a few days.
You’ll have instant access to specialists across the world as Lemon.io finds Computer Vision talent in multiple time zones from over 50 countries.
Lemon.io’s internal team thoroughly screens & selects programmers — our acceptance rate for candidates is just 1%.
We have dedicated recruiters constantly searching through over 300 websites, communities, and talent pools to curate a network of talented engineers, reducing your hiring time considerably.
To top it off, we provide no-risk substitutions of developers in case your selected individual cannot continue for any reason, ensuring seamless replacements of talent.
The Computer Vision Specialist hiring process stages:
1. Applicants describe their research background in computer vision algorithms, machine learning methods and related applications through detailed profiles which are screened based on necessary requirements.
2. Each CV is evaluated by recruiters to verify experience and qualifications via pre-employment checks, which may include a review of research publications or projects for those with a background in this type of work.
3. Candidates who make it to the shortlist take a screening call with a targeted technical test on Coderbyte. They are tested on computer vision algorithms, image processing, and machine learning concepts.
4. The highest-performing candidates then meet one-on-one with a tech interviewer, who may provide an algorithm challenge or go through some relevant project work to check for real-world experience and problem-solving capabilities.
Computer Vision specialists leverage AI (artificial intelligence) technology to look at images or videos and automatically “understand” what’s happening.
This is a cutting-edge technology useful across a large range of fields including healthcare (imagine automatically analyzing medical imaging), automotive (think self-driving systems), and marketing/advertising (consider the insights generated by being able to see which store displays resonate most with customers, then extrapolate those learnings across locations).
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