Founding AI Engineer (Agentic AI)
3y 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
The candidate is a self-driven full-stack and AI tooling engineer who has built genuinely impressive AI systems — a multimodal RAG platform and an LLM-powered video editing plugin — that are directly relevant to AlpacaRelay's content creation mission. Their 5 years at Atos provide solid engineering foundations with measurable DevOps and product delivery impact. The primary gap is the absence of experience with the specific agentic AI frameworks (LangGraph, LangSmith, LangFuse, CrewAI, LlamaIndex) that define this role, and the lack of verifiable code samples creates validation risk for a founding-level hire. However, their trajectory — expanding deeply into AI on their own initiative while holding a DevOps role — suggests high growth potential and genuine passion for the space. They warrants a technical interview with a mandatory coding/architecture assessment, particularly around agentic AI design patterns, before a final decision is made.
Top Strengths
- ✓Genuine end-to-end AI product delivery — Memex (multimodal RAG) and Ambar (LLM video editing plugin) are non-trivial systems that closely mirror AlpacaRelay's content creation product domain
- ✓Strong multimodal AI foundation (Whisper, CLIP, hybrid search, knowledge graphs) is directly aligned with text and image generation products
- ✓Full-stack + DevOps ownership mindset with quantified business impact — well-suited to the 'own the full lifecycle' expectation of a founding engineer
- ✓Self-motivated learner who has expanded significantly beyond their job title into AI tooling without formal AI role support
- ✓Multilingual (Amharic native, English C2, Polish A2) with international work experience — valuable for a remote, distributed team
Key Concerns
- !Absence of experience with the specific agentic AI frameworks (LangGraph, LangSmith, LangFuse, CrewAI, LlamaIndex) that form the core of this role's technical requirements — a learning curve exists
- !No verifiable code artifacts (no GitHub, no portfolio link) make it difficult to independently validate the technical depth described in the resume, which is a risk for a founding-level hire
Culture Fit
Growth Potential
High
Salary Estimate
$70,000 - $95,000 USD (Poland-based remote; may expect European or hybrid compensation benchmarks)
Assessment Reasoning
The candidate is rated BORDERLINE at 62/100. They clears the minimum experience threshold (5 years total, ~3 years AI-relevant) and demonstrates real AI product delivery in a domain closely aligned with AlpacaRelay's focus. Their multimodal RAG and LLM integration skills are genuine and sophisticated. However, they falls short of a FIT rating for two material reasons: (1) they lack hands-on experience with the specific agentic AI frameworks (LangGraph, LangSmith, LangFuse, CrewAI, LlamaIndex, MCP servers) that are explicitly central to this role — not just nice-to-haves — and (2) no code samples or GitHub activity were provided to independently validate their technical claims, which is a significant risk for a founding engineer role where hiring mistakes are costly. They should not be rejected outright given the quality of their project work and their clear upward trajectory into AI, but they should not advance without a rigorous technical screen including a take-home or live coding exercise specifically testing agentic AI architecture and framework familiarity.
Interview Focus Areas
Code Review
No code samples or GitHub profile were submitted, making direct code quality assessment impossible. Based on project descriptions alone, the candidate demonstrates architecturally sophisticated thinking with multi-service orchestration, evaluation harnesses, and hybrid retrieval pipelines. However, the absence of any reviewable code is a notable gap for a founding engineer role where hands-on technical depth must be verified. A coding assessment or GitHub review should be a mandatory step before advancing.
- +Project architecture descriptions reveal sophisticated system design thinking — the 5-service containerized architecture for Memex (FastAPI, Celery, Qdrant, Neo4j, Redis) shows production-level engineering judgment
- +Demonstrated ability to solve hard API constraints creatively (UXP + CEP hybrid IPC workaround for Adobe limitations) indicates strong problem-solving capability
- -No GitHub profile or code samples were provided, so code quality cannot be directly assessed — all evaluation is inferred from project descriptions, which may overstate actual implementation quality
Experience Overview
5y total · 3y relevantThe candidate is a capable full-stack and DevOps engineer who has meaningfully extended into AI tooling, producing two impressive personal projects that demonstrate genuine RAG, multimodal, and LLM integration skills. However, their professional title has been DevOps/Software Engineer, and the specific agentic AI frameworks (LangGraph, CrewAI, LlamaIndex) central to this role are absent from their stack. Their project work shows strong initiative and technical depth, but the gap between their current toolset and the required agentic framework ecosystem is the primary risk factor.
Matching Skills
Skills to Verify