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

The candidate is a Middle QA Engineer with approximately 3 years of experience in software testing, currently employed at Pivots Global. While they demonstrate competence in their current field — particularly in manual, functional, and API testing — their profile is entirely misaligned with the Applied AI Researcher / Founding Engineer role. The position requires deep expertise in AI/ML systems, LLMs, model training and deployment, cloud infrastructure, and technical leadership at a founding level. The candidate has no background in any of these areas. Their Python usage is limited to test automation scripting, which is not transferable to the AI/ML engineering demands of this role. There is also no evidence of publications, open-source AI contributions, leadership experience, or community involvement that the role prioritizes. This candidate is not a fit for this position in any practical sense.

Top Strengths

✓Master's degree in engineering provides a baseline technical foundation
✓Hands-on Python experience with automation frameworks
✓Methodical approach to testing and quality assurance demonstrated across large test case volumes
✓Cross-platform experience (web, mobile, iOS, Android) shows adaptability
✓Experience at a reputable tech company (Yandex) indicates a professional working environment

Key Concerns

!Complete absence of AI/ML knowledge, experience, or demonstrable interest — this is the core requirement of the role
!No leadership, architectural, or founding-level experience; role requires someone who can own the entire technical foundation of a company

Culture Fit

20%

Growth Potential

Low

Salary Estimate

$30,000 - $55,000 (aligned with mid-level QA engineer compensation in Eastern Europe/Serbia)

Assessment Reasoning

The candidate is assessed as NOT_FIT with high confidence (97%). The Applied AI Researcher / Founding Engineer role requires a candidate with deep AI/ML expertise, experience with LLMs and deep learning architectures, PyTorch/TensorFlow proficiency, cloud infrastructure management, model lifecycle ownership, and the ambition and capability to serve as a founding technical leader. The candidate's entire professional background is in QA engineering — a critically important but entirely different discipline. They have no demonstrable AI/ML knowledge, no relevant publications or open-source contributions, no architectural or leadership experience, and their Python skills are scoped exclusively to test automation. The overlap between their profile and the role requirements is less than 5%. Even accounting for transferable skills and growth potential, the gap is too fundamental to bridge within the context of this senior-level, founding-engineer role. The overall score of 12/100 reflects this near-total mismatch.

Interview Focus Areas

Understanding of AI/ML fundamentals (if any self-study or interest exists)Career goals and whether there is a genuine pivot intention toward AI research

Code Review

PoorJunior Level

No code examples or GitHub profile were submitted, making a direct assessment impossible. The Python experience described is limited to test automation frameworks and is not indicative of the deep software engineering or AI/ML modeling capabilities required for this role. There is no evidence of experience with PyTorch, TensorFlow, or any ML-oriented codebase.

PythonSeleniumpytestAppium

+Has exposure to Python scripting in a test automation context

-No code samples or GitHub profile provided to assess engineering quality
-Python usage is limited to QA tooling (Selenium, pytest, Appium) — not AI/ML development

Experience Overview

3y total · 0y relevant

The candidate is a capable QA Engineer with 3 years of experience in manual and automation testing across web and mobile platforms. However, their background has no overlap with the Applied AI Researcher / Founding Engineer role, which demands deep expertise in AI/ML, LLMs, model training, and cloud infrastructure. There is a fundamental domain mismatch between their skillset and the position requirements.

Matching Skills

Python (basic/automation scripting)

Skills to Verify

AI/ML research or engineering experienceLLMs or large language model experienceMultimodal models (text, vision, speech)Deep learning architecturesPyTorch or TensorFlowCloud infrastructure (AWS/GCP/Azure)Model training and fine-tuningModel lifecycle managementMLOps pipelinesPhD or strong academic background in CS/AI/MathOpen-source AI contributionsSystem architecture designLeadership or team management experience

Candidate information is anonymized. Personal details are hidden for fair evaluation.