Hire Computer Vision Developers
Connect with Computer Vision Developers from Latin America. Experts in detection, tracking, and image processing with team setup in 21 days.
Hire Remote Computer Vision Developers
Isabella, a brilliant AI researcher, excels in innovative solutions and part-time projects.
- Computer Vision
- Reinforcement Learning
- NLP
- TensorFlow
- Deep Learning
Luis Alfredo is an AI enthusiast who develops scalable and functional tech solutions.
- AI Development
- Machine Learning
- System Design
- Data Integration
- Product Strategy
Fernanda is an AI strategist aligning smart technologies with product development.
- AI Strategy
- Tech Roadmapping
- Model Testing
- System Evaluation
- Cross-functional Planning
Florencia is an AI expert creating intelligent systems for real, practical applications.
- Predictive Modeling
- Data Engineering
- AI Applications
- Python & ML Libraries
- Data-Driven Development
Luis is a visionary AI researcher. His innovative solutions redefine the boundaries of technology.
- Reinforcement Learning
- Computer Vision
- NLP
- TensorFlow
- Deep Learning
Matías is a skilled prompt engineer, adept at crafting precise and impactful AI interactions.
- Python
- AI Ethics
- Data Labeling
- NLP
- LLMs
Luis is an AI generalist creating functional systems with real-world applications.
- AI Strategy
- Machine Learning
- System Design
- Product Roadmapping
- Problem Solving
"Over the course of 2024, we successfully hired 9 exceptional team members through Lupa, spanning mid-level to senior roles. The quality of talent has been outstanding, and we’ve been able to achieve payroll cost savings while bringing great professionals onto our team. We're very happy with the consultation and attention they've provided us."
“We needed to scale a new team quickly - with top talent. Lupa helped us build a great process, delivered great candidates quickly, and had impeccable service”
“With Lupa, we rebuilt our entire tech team in less than a month. We’re spending half as much on talent. Ten out of ten”
Lupa's Proven Process
Together, we'll create a precise hiring plan, defining your ideal candidate profile, team needs, compensation and cultural fit.
Our tech-enabled search scans thousands of candidates across LatAm, both active and passive. We leverage advanced tools and regional expertise to build a comprehensive talent pool.
We carefully assess 30+ candidates with proven track records. Our rigorous evaluation ensures each professional brings relevant experience from industry-leading companies, aligned to your needs.
Receive a curated selection of 3-4 top candidates with comprehensive profiles. Each includes proven background, key achievements, and expectations—enabling informed hiring decisions.
Reviews
"Over the course of 2024, we successfully hired 9 exceptional team members through Lupa, spanning mid-level to senior roles. The quality of talent has been outstanding, and we’ve been able to achieve payroll cost savings while bringing great professionals onto our team. We're very happy with the consultation and attention they've provided us."
“We needed to scale a new team quickly - with top talent. Lupa helped us build a great process, delivered great candidates quickly, and had impeccable service”
“With Lupa, we rebuilt our entire tech team in less than a month. We’re spending half as much on talent. Ten out of ten”
“We scaled our first tech team at record speed with Lupa. We couldn’t be happier with the service and the candidates we were sent.”
"Recruiting used to be a challenge, but Lupa transformed everything. Their professional, agile team delivers top-quality candidates, understands our needs, and provides exceptional personalized service. Highly recommended!"
“Lupa has become more than just a provider; it’s a true ally for Pirani in recruitment processes. The team is always available to support and deliver the best service. Additionally, I believe they offer highly competitive rates and service within the market.”
"Highly professional, patient with our changes, and always maintaining clear communication with candidates. We look forward to continuing to work with you on all our future roles."
“Lupa has been an exceptional partner this year, deeply committed to understanding our unique needs and staying flexible to support us. We're excited to continue our collaboration into 2025.”
"What I love about Lupa is their approach to sharing small, carefully selected batches of candidates. They focus on sending only the three most qualified individuals, which has already helped us successfully fill 7 roles.”
"We hired 2 of our key initial developers with Lupa. The consultation was very helpful, the candidates were great and the process has been super fluid. We're already planning to do our next batch of hiring with Lupa. 5 stars."
"Working with Lupa for LatAm hiring has been fantastic. They found us a highly skilled candidate at a better rate than our previous staffing company. The fit is perfect, and we’re excited to collaborate on more roles."
"We compared Lupa with another LatAm headhunter we found through Google, and Lupa delivered a far superior experience. Their consultative approach stood out, and the quality of their candidates was superior. I've hired through Lupa for both of my companies and look forward to building more of my LatAm team with their support."
“We’ve worked with Lupa on multiple roles, and they’ve delivered time and again. From sourcing an incredible Senior FullStack Developer to supporting our broader hiring needs, their team has been proactive, kind, and incredibly easy to work with. It really feels like we’ve gained a trusted partner in hiring.”
Working with Lupa was a great experience. We struggled to find software engineers with a specific skill set in the US, but Lupa helped us refine the role and articulate our needs. Their strategic approach made all the difference in finding the right person. Highly recommend!
Lupa goes beyond typical headhunters. They helped me craft the role, refine the interview process, and even navigate international payroll. I felt truly supported—and I’m thrilled with the person I hired. What stood out most was their responsiveness and the thoughtful, consultative approach they brought.
Computer Vision Developers Soft Skills
Analytical Thinking
Break down image-based problems into solvable units.
Patience
Iterate through model training and debugging cycles.
Attention to Detail
Catch edge cases in image processing and labeling.
Communication
Explain CV results to stakeholders with clarity.
Collaboration
Work with product teams to align outputs to user goals.
Curiosity
Explore new models, datasets, and approaches to vision tasks.
Computer Vision Developers Skills
Image Recognition
Develop systems for object detection and classification.
OpenCV & Deep Learning
Use OpenCV, PyTorch, or TensorFlow for vision tasks.
Model Deployment
Deploy vision models to edge or cloud environments.
Image Segmentation
Implement models to isolate objects in visual input.
Video Analysis
Extract and track features across video frames.
Data Annotation Tools
Work with labeling tools to train accurate models.
How to Write an Effective Job Post to Hire Computer Vision Developers
Recommended Titles
- Computer Vision Engineer
- Image Processing Developer
- Vision AI Developer
- Machine Vision Engineer
- CV/ML Engineer
- Video Analytics Developer
Role Overview
- Tech Stack: Experienced in OpenCV, TensorFlow, Python, and YOLO/Detectron2.
- Project Scope: Build image and video processing models for object detection and analysis.
- Team Size: Join a CV team of 4–6 working on industry-specific visual pipelines.
Role Requirements
- Years of Experience: At least 3 years in computer vision model development.
- Core Skills: Image segmentation, detection algorithms, and data annotation workflows.
- Must-Have Technologies: OpenCV, PyTorch, YOLO, AWS Rekognition, NumPy.
Role Benefits
- Salary Range: $100,000 – $160,000 depending on visual pipeline experience.
- Remote Options: Global remote, with core hours aligned to US East or West time.
- Growth Opportunities: Build real-world CV solutions in healthcare, retail, and security sectors.
Do
- Include core CV skills: detection, tracking, segmentation
- Mention toolkits like OpenCV, TensorFlow, or PyTorch
- Highlight applications in real-time video or image analysis
- Show career growth in AI perception systems
- Use domain-specific and technically accurate terms
Don't
- Don’t post a generic ML job without CV-specific skills
- Avoid vague mentions of “image analysis” without tools
- Don’t omit use of OpenCV, PyTorch, or model tuning
- Refrain from ignoring real-time application examples
- Don’t use overly academic language without projects
Top Computer Vision Developer Interview Questions
How to screen Computer Vision Developer capabilities
What libraries and tools do you use for computer vision?
Look for OpenCV, TensorFlow, PyTorch, or YOLO. Bonus for mentioning real-time processing or cloud deployment.
How do you handle image preprocessing in pipelines?
Expect normalization, augmentation, resizing, and annotation workflows to improve model performance.
Describe a computer vision project you've worked on.
They should outline problem scope, dataset, model architecture, evaluation, and deployment outcomes.
What methods do you use for object detection?
Look for knowledge of YOLO, SSD, Faster R-CNN, and understanding of trade-offs in accuracy and speed.
How do you optimize models for inference on edge devices?
Expect pruning, quantization, and lightweight architectures. Bonus if they’ve used TensorRT or TFLite.
How do you debug poor object detection performance?
Look for inspection of annotation quality, class imbalance, anchor box tuning, and test set evaluation.
Describe a time you handled noisy or low-quality image data.
Expect preprocessing steps like denoising, contrast enhancement, and data augmentation strategies.
What’s your strategy when a model performs well on training data but poorly in production?
Expect analysis of domain shift, data drift, and retraining with real-world inputs.
How do you debug edge deployment issues in vision applications?
Look for quantization handling, hardware acceleration constraints, and model-light alternatives.
How do you approach optimizing inference speed for real-time tasks?
Expect batch processing, resolution trade-offs, GPU utilization, or ONNX/TensorRT pipelines.
Tell me about a time you optimized a slow-performing vision pipeline.
Look for diagnosis skills, benchmarking, and collaboration with engineering for speedup.
Describe a project where your model had biased outputs.
Expect ethical reflection, dataset evaluation, and model improvement steps.
How do you handle pressure when your model misses critical detections?
Expect accountability, logging improvements, and monitoring strategies.
Describe how you explain complex model behavior to non-technical teams.
Expect clear analogies, impact framing, and focus on outputs and trade-offs.
Have you ever had to pivot your technical strategy mid-project?
Expect agility, documentation of trade-offs, and alignment with new constraints.
- Inconsistent preprocessing of image data
- Ignores edge-case performance in model evaluation
- Lack of understanding of camera/environment constraints
- Weak debugging skills in detection/segmentation tasks
- Failure to explain computer vision pipelines clearly
Build elite teams in record time, full setup in 21 days or less.
Book a Free ConsultationWhy We Stand Out From Other Recruiting Firms
From search to hire, our process is designed to secure the perfect talent for your team
Local Expertise
Tap into our knowledge of the LatAm market to secure the best talent at competitive, local rates. We know where to look, who to hire, and how to meet your needs precisely.
Direct Control
Retain complete control over your hiring process. With our strategic insights, you’ll know exactly where to find top talent, who to hire, and what to offer for a perfect match.
Seamless Compliance
We manage contracts, tax laws, and labor regulations, offering a worry-free recruitment experience tailored to your business needs, free of hidden costs and surprises.
Lupa will help you hire top talent in Latin America.
Book a Free Consultation
Top Computer Vision Developer Interview Questions
How to screen Computer Vision Developer capabilities
What libraries and tools do you use for computer vision?
Look for OpenCV, TensorFlow, PyTorch, or YOLO. Bonus for mentioning real-time processing or cloud deployment.
How do you handle image preprocessing in pipelines?
Expect normalization, augmentation, resizing, and annotation workflows to improve model performance.
Describe a computer vision project you've worked on.
They should outline problem scope, dataset, model architecture, evaluation, and deployment outcomes.
What methods do you use for object detection?
Look for knowledge of YOLO, SSD, Faster R-CNN, and understanding of trade-offs in accuracy and speed.
How do you optimize models for inference on edge devices?
Expect pruning, quantization, and lightweight architectures. Bonus if they’ve used TensorRT or TFLite.
How do you debug poor object detection performance?
Look for inspection of annotation quality, class imbalance, anchor box tuning, and test set evaluation.
Describe a time you handled noisy or low-quality image data.
Expect preprocessing steps like denoising, contrast enhancement, and data augmentation strategies.
What’s your strategy when a model performs well on training data but poorly in production?
Expect analysis of domain shift, data drift, and retraining with real-world inputs.
How do you debug edge deployment issues in vision applications?
Look for quantization handling, hardware acceleration constraints, and model-light alternatives.
How do you approach optimizing inference speed for real-time tasks?
Expect batch processing, resolution trade-offs, GPU utilization, or ONNX/TensorRT pipelines.
Tell me about a time you optimized a slow-performing vision pipeline.
Look for diagnosis skills, benchmarking, and collaboration with engineering for speedup.
Describe a project where your model had biased outputs.
Expect ethical reflection, dataset evaluation, and model improvement steps.
How do you handle pressure when your model misses critical detections?
Expect accountability, logging improvements, and monitoring strategies.
Describe how you explain complex model behavior to non-technical teams.
Expect clear analogies, impact framing, and focus on outputs and trade-offs.
Have you ever had to pivot your technical strategy mid-project?
Expect agility, documentation of trade-offs, and alignment with new constraints.
- Inconsistent preprocessing of image data
- Ignores edge-case performance in model evaluation
- Lack of understanding of camera/environment constraints
- Weak debugging skills in detection/segmentation tasks
- Failure to explain computer vision pipelines clearly
