Computer Vision Engineer
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At a glance
What you'll be doing
Hands-on Computer Vision Engineer role in Ireland (hybrid), building practical vision solutions for advanced manufacturing. Involvement spans the full development cycle, collaborating with multidisciplinary teams across software, embedded, electronics, optics, and product engineering.
Key requirements
Must-have
- Solid background in computer vision or image processing
- Strong programming experience in Python and C++
- Experience working in Linux environments
- Hands-on exposure to OpenCV and at least one major ML or deep learning framework
- Good grasp of algorithms, data structures, and object-oriented design
Nice-to-have
- Experience with embedded or performance-constrained systems
- Exposure to cameras, optics, or lighting
Role signals
- Technical focus
- computer vision, image processing, multidisciplinary engineering
- Hands-on vs management
- Hands-on
Similar jobs
Full job description
Computer Vision Engineer
Industrial technology
Greater Dublin (Hybrid)
IT Search has partnered with a growing Irish engineering company that is expanding its vision and AI team to support increased demand for its inspection and measurement platforms used in advanced manufacturing environments.
This is a hands-on role within a multidisciplinary engineering team, focused on building practical vision solutions that are deployed directly into customer environments. You’ll be involved across the full development cycle, from early-stage concepts and prototyping through to production-ready systems, working closely with colleagues across software, embedded, electronics, optics, and product engineering.
They’re looking for someone with:
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A solid background in computer vision or image processing
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Strong programming experience in Python and C++
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Experience working in Linux environments
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Hands-on exposure to OpenCV and at least one major ML or deep learning framework
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A good grasp of algorithms, data structures, and object-oriented design
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Any experience with embedded or performance-constrained systems is beneficial
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Exposure to cameras, optics, or lighting would be a strong advantage
The role offers a salary in line with the market, along with bonus, pension, healthcare, and a hybrid working model within a supportive, technically strong team.
If this sounds like your next move, apply via the link or contact Graeme King at IT Search for a confidential chat.
Apply Now
Interview prep pack
Grounded in this listing. Use it to prepare examples before you apply.
Your interview focus
You will be assessed on your ability to design and implement computer vision solutions for industrial inspection and measurement, using Python, C++, OpenCV, and ML frameworks in a Linux environment. Collaboration with multidisciplinary teams and practical deployment experience are key.
- Computer vision algorithms·High
- Python & C++ programming·High
- Linux development·High
- OpenCV & ML frameworks·High
Only have 30 minutes?
Follow a focused preparation plan based on this job.
Start 30-minute prep
Your 30-minute plan
Review Computer Vision Projects
0–8 minList and summarise your most relevant vision projects, focusing on your role, challenges, and outcomes.
Refresh Python, C++, and OpenCV Skills
8–15 minSkim recent code or tutorials in Python and C++, especially using OpenCV for image processing.
Prepare for Multidisciplinary Collaboration
15–23 minRecall examples of working with other engineering disciplines and how you communicated technical ideas.
Draft Questions for the Interviewer
23–30 minWrite down questions about the team's workflow, technology choices, and learning opportunities.
Talking points
, 6 itemsComputer Vision Fundamentals
You need to demonstrate a strong grasp of image processing and vision algorithms, as these are core to the role.
Python and C++ for Vision Applications
The job requires hands-on coding in both languages for building and optimising vision systems.
OpenCV and ML/Deep Learning Frameworks
Practical experience with OpenCV and at least one major ML framework is explicitly required.
Linux Development Environment
You will be working in Linux, so familiarity with its tools and workflows is essential.
Algorithms, Data Structures, and OOP
A good understanding of these concepts is necessary for efficient and maintainable code.
Embedded and Performance-Constrained Systems
Experience here is beneficial, as solutions may need to run on limited hardware.
What to research
, 4 itemsEnd-to-End Vision Solution Development
Be ready to discuss your experience taking vision projects from concept through prototyping to production deployment, especially in industrial or practical settings.
Python, C++, and OpenCV Integration
Expect questions on how you use Python and C++ together in vision pipelines, and your hands-on experience with OpenCV and ML frameworks.
Multidisciplinary Teamwork
Prepare examples of collaborating with engineers from software, embedded, electronics, and optics backgrounds, and how you contributed to team outcomes.
Performance Optimisation for Embedded Systems
If you have experience with embedded or resource-constrained environments, be ready to discuss optimisation strategies and relevant projects.
Questions to ask
, 6 itemsWhat are the biggest technical challenges your vision team is currently facing?
Why ask this? Reveals the complexity and focus areas of the work.
How does the team collaborate across software, embedded, electronics, and optics disciplines?
Why ask this? Clarifies how multidisciplinary teamwork is structured.
Which ML or deep learning frameworks are most commonly used in your current projects?
Why ask this? Helps you understand the specific tools you'll be working with.
How are new vision solutions prototyped and validated before deployment?
Why ask this? Shows the development and testing process.
What opportunities exist for learning about optics and lighting if I have less experience in that area?
Why ask this? Indicates support for skill development.
How is success measured for deployed inspection and measurement systems?
Why ask this? Clarifies expectations and performance metrics.
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