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 highly capable applied AI engineer with 7 years of experience and a strong founding-engineer profile. Their two CTO stints, diverse AI system delivery record (medical imaging, LLMs, multimodal apps), and hands-on full-stack ownership make them a compelling candidate for this role. The primary gaps are the absence of a PhD (preferred but not required), lack of public code or open-source presence, and a career pattern that suggests contract/consulting engagements rather than long-term organizational commitment. However, their technical breadth, startup DNA, and alignment with the role's founding-engineer scope position them as a strong FIT candidate pending a rigorous technical interview to validate code quality and research depth.

Top Strengths

✓Proven founding CTO experience with two AI startups, demonstrating both technical depth and startup leadership maturity
✓Full-stack AI ownership: from data collection and model training to cloud deployment and production monitoring
✓Hands-on multimodal and LLM expertise including text generation, image generation (FLUX, SegFormer), and agentic systems
✓Demonstrated ability to move fast and ship in highly ambiguous environments — solo iOS app launch, enterprise refactor in 2 months
✓Strong alignment with role responsibilities: architecture decisions, team leadership, model lifecycle, and collaboration with leadership

Key Concerns

!No PhD and Master's still in progress — falls short of the academic credential strongly preferred for this research-oriented founding role
!No public code, GitHub, or open-source presence makes it difficult to independently verify engineering quality, a critical factor for a founding engineer

Culture Fit

80%

Growth Potential

High

Salary Estimate

$90,000 - $130,000 (within posted range; likely mid-to-upper band given CTO history and 7 years experience, but offset by no PhD and contractor-heavy history)

Assessment Reasoning

The candidate scores 82/100 and is assessed as FIT based on strong alignment across the core requirements: 7 years of AI/ML experience, proven delivery of production AI systems (consumer app, enterprise NLP, medical vision), hands-on Python/PyTorch/LLM expertise, cloud infrastructure experience (Azure), and two founding CTO roles demonstrating both technical and leadership credentials. They meets or exceeds the 3–7 year experience requirement, has built and managed teams, and has shipped working AI systems across multiple domains. The role explicitly targets someone who wants to own the full technical foundation of an early-stage company — which is exactly the candidate's background. The key gaps (no PhD, no GitHub/open-source, short tenures) are real but not disqualifying: the PhD is preferred not required, and the open-source gap can be validated via a technical interview. The future-dated Finastra role and contractor-heavy pattern should be addressed in screening but do not constitute red flags. Overall, this is a strong FIT candidate who should advance to a technical interview round.

Interview Focus Areas

Deep technical dive into a past AI system architecture — specifically the multi-agent LLM or 3D segmentation pipeline — to assess design thinking and code qualityAssessment of research depth: ability to read, implement, and extend cutting-edge papers (LLMs, diffusion models, multimodal architectures)Explore leadership philosophy and how they managed engineers and doctors at AIMinded under resource constraintsClarify the Finastra future-dated employment and contractor vs. full-time commitment preferencesEvaluate ambition and fit for C-level growth trajectory within an early-stage startup

Code Review

FairSenior Level

No code example or GitHub profile was submitted, making a direct code quality assessment impossible. Based on the complexity and scale of systems described — multi-agent pipelines, 3D segmentation models, iOS apps built solo — the candidate likely operates at a senior engineering level, but this must be validated through a technical interview or take-home assessment. The absence of any public code is a meaningful gap for a founding engineer role.

PythonPyTorchFastAPILangChainLangGraphDockerSwift / SwiftUITensorFlowSQLAlchemyRedis

+Technology stack choices described in CV suggest familiarity with production-grade, modular architectures (LangGraph, FastAPI, Redis, Docker)
+Demonstrated ability to refactor legacy systems for significant accuracy gains (90% → 96% at Finastra in 2 months)

-No code sample, GitHub profile, or open-source repository provided — code quality cannot be directly assessed
-Cannot verify clean, modular coding practices or engineering standards from available materials alone

Experience Overview

7y total · 6y relevant

The candidate presents a compelling 7-year track record in applied AI engineering with two CTO stints, covering LLMs, multimodal systems, medical imaging, and agentic architectures. Their experience closely mirrors the role's demands — building from scratch, owning the full technical stack, and shipping to real users. The absence of a PhD and verified open-source contributions are the primary gaps against stated preferences, but their practical depth largely compensates.

Matching Skills

PythonPyTorchLLMsMulti-Agent SystemsLangChain / LangGraphRAG (Retrieval-Augmented Generation)FastAPIDockerAzure CloudPostgreSQLModel fine-tuningComputer Vision / Deep LearningETL / Data PipelinesMultimodal models (vision + text)Startup founding / CTO experienceTeam leadershipModel lifecycle managementPrompt EngineeringGenerative AI (image and text)

Skills to Verify

PhD or formal postgraduate AI/ML qualification (Master's in progress, not complete)AWS or GCP experience (Azure only visible)Explicit MLOps pipeline tooling (e.g., MLflow, Kubeflow)GitHub / open-source contributions not demonstratedTensorFlow (primarily PyTorch)

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