Founding AI Engineer (Agentic AI)

2y relevant experience

Under Review

For hiring agencies & HR teams

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 an experienced senior full-stack engineer with 11+ years of experience and valuable exposure to ML platform environments through Dataiku. Their engineering fundamentals — Python, Docker, Kubernetes, microservices, CI/CD — are solid and relevant. However, the core requirement of this founding AI engineer role is deep, hands-on experience with modern agentic AI stacks (LangGraph, LangSmith, RAG, vector databases, OpenAI/Anthropic APIs), and the candidate's resume provides no direct or verifiable evidence of this. The complete absence of public technical artifacts (GitHub, open-source, portfolio) further limits the ability to assess their actual AI engineering depth. They are a BORDERLINE candidate who could be compelling if they can demonstrate substantial hands-on LLM and agentic AI work in an interview, but without that validation, the fit risk is high for a founding technical role.

Top Strengths

✓11+ years of professional software engineering experience with strong full-stack and backend foundations
✓Direct exposure to ML/AI platform engineering at Dataiku, including model deployment pipelines and AI feature integration
✓Demonstrated technical leadership — coordinating squads, mentoring engineers, owning architectural decisions
✓Production-grade DevOps competency (Docker, Kubernetes, CI/CD) that is valuable for deploying AI systems
✓Polyglot background and microservices experience suggesting strong system design intuition

Key Concerns

!Critical gap in the specific agentic AI stack required (LangGraph, LangSmith, RAG, vector databases, LLM APIs, MCP servers) with no verifiable evidence of hands-on LLM engineering
!Complete absence of public technical presence (no GitHub, no open-source work, no portfolio) makes it impossible to validate AI engineering claims independently

Culture Fit

60%

Growth Potential

Moderate

Salary Estimate

$80,000 - $100,000 (mid-range of posted band, given strong general engineering background but unverified AI-specific depth)

Assessment Reasoning

The candidate is rated BORDERLINE rather than NOT_FIT because their 11-year engineering background, production DevOps skills, and Dataiku experience (an ML platform company) show genuine transferable value and suggest they are not a novice to the AI space. However, they falls short of a FIT decision because the core of this role — hands-on agentic AI development using LangGraph, LangSmith, LangFuse, RAG, vector databases, MCP servers, and LLM APIs — is entirely absent from their resume and cannot be verified through any public technical presence. The resume summary mentions 'agentic AI applications' but provides no supporting detail, tools, or outcomes. For a founding engineer role where the hire must hit the ground running on cutting-edge agentic AI architecture, this gap is material. A technical interview specifically probing LLM engineering depth is strongly recommended before any advancement decision.

Interview Focus Areas

Deep dive into any hands-on experience with LLMs, agentic frameworks, or RAG — what specific tools were used, what was built, and what was the outcomeProbe the 'agentic AI applications' claim in the resume summary: what exactly was built, what LLM stack was used, and can any artifacts be sharedAssess startup mentality and ownership mindset — founding engineer roles require a different operating mode than large enterprise or remote-squad environmentsEvaluate cloud infrastructure experience (AWS/GCP) and whether there is any experience with AI observability or evaluation frameworks

Code Review

FairSenior Level

No code example or GitHub profile was provided, preventing any direct assessment of code quality or AI engineering capability. This is a meaningful gap for a founding technical role where hands-on AI system building must be verifiable. The score reflects the inability to evaluate rather than a negative signal per se, but the lack of public technical presence is itself a mild concern for a senior founding engineer candidate.

+Resume demonstrates familiarity with software engineering best practices such as code reviews, documentation, and unit testing
+Experience with production-grade tooling (Docker, Kubernetes, CI/CD) suggests competent deployment and operational code discipline

-No code example, GitHub profile, or open-source contributions were provided, making it impossible to directly assess code quality, AI-specific implementations, or LLM engineering skills
-For a founding AI engineering role requiring proven production AI system development, the absence of any demonstrable work is a significant evaluation gap

Experience Overview

11y total · 2y relevant

The candidate is a seasoned full-stack engineer with 11 years of experience and meaningful exposure to ML workflows through their work at Dataiku. However, their resume lacks direct evidence of hands-on experience with the modern agentic AI stack (LangGraph, RAG, vector databases, LLM APIs) that is central to this founding role. The gap between claimed 'agentic AI' experience and the absence of any supporting tools or projects is a notable concern.

Matching Skills

PythonPostgreSQLDockerKubernetesCI/CDReactNode.jsDjangoMicroservicesMachine Learning Pipeline Deployment

Skills to Verify

LangGraphLangSmithLangFuseCrewAILlamaIndexVector DatabasesRAG (Retrieval-Augmented Generation)MCP Servers and Tool IntegrationsOpenAI APIsAnthropic APIsNumPySciPyAWS and/or GCPGitHub Actions CI/CDPrompt EngineeringAI ObservabilityAgent Orchestration

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