Senior Computer Vision Engineer
6y relevant experience
EU engineers, ready to place with your US clients
Pre-screened on AI. Remote B2B contracts. View 5 full profiles free — AI score, skills report, interview questions included.
Executive Summary
This candidate is a technically compelling Senior CV Engineer candidate with 7+ years of experience spanning academic research and industry applications across diverse domains. their depth in computer vision — from classical algorithms to modern diffusion models — combined with AWS ML certification and real-time deployment experience positions him well for this role. The primary gaps are in MLOps tooling, containerization, and verifiable production-scale pipeline ownership, which are important for this senior position. The informal resume presentation and absence of LinkedIn/GitHub are concerns for professional credibility but do not outweigh their technical substance. A structured technical interview and code assessment are strongly recommended to validate production engineering capability before making an offer decision.
Top Strengths
- ✓Rare combination of deep CV research expertise (IEEE publication, UAV object detection, SLAM) and industry application across energy, telco, and manufacturing sectors
- ✓Real-time computer vision deployment experience with custom protocols and embedded hardware, directly relevant to high-volume production pipelines
- ✓AWS Certified ML Professional demonstrating validated cloud deployment knowledge
- ✓Exposure to cutting-edge generative AI techniques (diffusion model distillation, Stable Diffusion optimization) showing continuous learning and adaptability
- ✓Creative application of CV in live performance contexts demonstrates end-to-end ownership, problem-solving under constraints, and passion for the craft
Key Concerns
- !Absence of GitHub, LinkedIn, and code samples creates significant verification gaps for a senior engineering role requiring demonstrated production-grade software engineering
- !Predominantly contract and research-based roles raise questions about experience owning and maintaining long-running production ML systems with SLA requirements, team mentorship, and architectural decision-making at scale
Culture Fit
Growth Potential
High
Salary Estimate
$110k-$135k
Assessment Reasoning
This candidate is assessed as FIT with an overall score of 82. they meets or exceeds the core technical requirements: 7+ years of experience (above the 6-year minimum), strong PyTorch/TensorFlow proficiency, deep CNN architecture knowledge, real-time CV deployment, and AWS ML certification. their IEEE publication on object detection and hands-on work with SLAM, diffusion models, and embedded hardware demonstrate senior-level technical depth. Preferred qualifications in object detection, face recognition, and W&B familiarity are also met. Deductions are applied for the absence of verifiable MLOps/containerization experience, lack of mentorship evidence, missing GitHub/LinkedIn presence, and an informal resume presentation that introduces some uncertainty around professional communication fit. The candidate clears the 70-point FIT threshold and warrants advancement to a technical screen and coding assessment, with interview focus on production engineering practices and leadership evidence.
Interview Focus Areas
Code Review
No code samples, GitHub profile, or portfolio links were provided, significantly limiting the ability to assess code quality objectively. The technology stack listed is sophisticated and well-chosen for production CV work, and the real-time systems described (facial tracking pipelines, SLAM with custom protocols) imply meaningful engineering capability. A technical code assessment is strongly recommended before advancing this candidate.
- +Demonstrated use of production-grade libraries (SCRFD, ONNX, FastSLAM 2.0, Ray, DeepSpeed, Optuna)
- +Real-time system implementation with custom transmission protocols suggests systems-level coding competence
- +Breadth of hardware experience (embedded devices, GPU clusters) suggests practical engineering beyond pure research
- -No GitHub profile or public code samples provided, making it impossible to assess actual code quality, style, or engineering practices
- -No evidence of code reviews, CI/CD contributions, or collaborative software engineering at scale
- -Research-oriented codebase experience may not translate directly to production-grade, maintainable codebases
Experience Overview
7y total · 6y relevantStefanos presents a genuinely impressive and varied CV with 7+ years spanning research and industry computer vision work, including IEEE publication, real-time CV deployments, and AWS ML certification. their technical depth in CV fundamentals, PyTorch/TensorFlow, and specialized domains (SLAM, diffusion models, object detection) is well above average for the role. However, gaps in MLOps tooling, containerization, and explicit large-scale production pipeline experience temper an otherwise strong technical profile.
Matching Skills
Skills to Verify