Image Recognition Engineer
5y 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 well-rounded Senior ML Engineer whose career arc uniquely combines early computer vision engineering with mature MLOps capabilities — a rare pairing that maps well onto this role's end-to-end pipeline ownership requirement. their direct experience deploying semantic segmentation and object detection models (Triebwerk) alongside production-grade inference systems (ArchetypeAI) demonstrates they can bridge research and deployment. The primary risk is that active CV model development appears to have taken a back seat to MLOps over the past two years, so their familiarity with the latest classification architectures and training workflows should be validated. Assuming a positive technical screen, Danijel has the background, autonomy orientation, and startup experience to be a high-impact contributor to a 4-person ML team in a remote EU-first environment.
Top Strengths
- ✓Production MLOps mastery: end-to-end pipeline ownership, inference APIs, CI/CD, monitoring, and Kubernetes — directly aligned with deployment requirements
- ✓Hands-on computer vision engineering: built and deployed semantic segmentation and object detection models for a live consumer application
- ✓Cloud infrastructure fluency across AWS and GCP with advanced containerization skills (Docker, K8s)
- ✓Academic rigor demonstrated through PhD studies, 10/10 GPA at bachelor and master levels, and two published papers
- ✓Proven adaptability across multiple ML domains (CV, MLOps, data science, game analytics) in diverse startup environments
Key Concerns
- !Active computer vision model development appears to have paused ~2 years ago; recent work is predominantly MLOps-focused, raising questions about currency of CV architecture knowledge (especially transformers, EfficientNet, modern classification pipelines)
- !No code portfolio available for technical validation — the absence of GitHub or any submitted code sample introduces meaningful uncertainty about hands-on engineering quality
Culture Fit
Growth Potential
High
Salary Estimate
$85k-$105k
Assessment Reasoning
This candidate is assessed as FIT (score 78) based on: (1) direct professional experience in computer vision engineering with state-of-the-art segmentation and detection models, (2) exceptional MLOps expertise covering the full production deployment lifecycle that the role explicitly requires, (3) strong alignment with the technical environment (PyTorch, Docker, K8s, AWS, GCP, OpenCV), (4) seniority and startup experience consistent with the mid-level experience range, and (5) cultural alignment with autonomy-driven, remote-first startup work. The score is modulated below 85 due to the two-year gap in active CV model development, absence of a code portfolio for validation, and uncertainty around classification-specific architecture depth (ViT, EfficientNet). A technical screening round is strongly recommended before extending an offer to confirm CV knowledge currency and Python/PyTorch coding quality.
Interview Focus Areas
Code Review
A meaningful code review assessment cannot be completed due to the absence of a GitHub profile or submitted code samples. The resume narrative suggests solid production engineering instincts (APIs, CI/CD, testing frameworks), and the candidate's senior-level trajectory implies reasonable coding competence. However, this remains a significant gap that should be addressed with a technical screening or take-home challenge before advancing.
- +Resume mentions building inference APIs with FastAPI, integration/load testing frameworks, and CI/CD pipelines — indicating production-grade engineering practices
- +One historical GitHub project linked in resume (linear regression in R) shows willingness to share work publicly
- -No GitHub profile submitted, making direct code quality assessment impossible
- -Single linked project is a basic R regression model from undergraduate studies — does not demonstrate current Python/PyTorch capabilities
- -Cannot evaluate code structure, documentation habits, or software engineering practices from available data
Experience Overview
8y total · 5y relevantThis candidate brings a compelling combination of direct computer vision engineering experience and mature MLOps skills that together cover the end-to-end pipeline this role requires. their CV work (semantic segmentation, object detection) and production deployment expertise are strong matches, though their recent years have leaned more heavily into MLOps and data science rather than active model research. The absence of a GitHub profile and explicit classification-architecture experience leaves some uncertainty about current CV depth.
Matching Skills
Skills to Verify