Founding AI Engineer (Agentic AI)

4y relevant experience

Qualified

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 a strong FIT candidate for the Founding AI Engineer role at AlpacaRelay. They bring 4 years of focused GenAI engineering experience with a technical stack that maps closely to the job requirements, including hands-on production experience with LangGraph, LangSmith, LangFuse, LlamaIndex, RAG architectures, and multi-LLM provider integrations. Their founding engineer background at multiple early-stage startups, combined with $2M+ in delivered business value and team leadership of up to 13 engineers, demonstrates the ownership mentality and execution capability this role demands. The primary gaps — no code sample submitted, limited classical ML tooling evidence, and absence of Kubernetes/MCP server experience — are addressable but should be validated before an offer is extended. A technical screen with a live or take-home coding component is strongly recommended to close the quality signal gap.

Top Strengths

✓Directly relevant agentic AI stack experience: LangGraph, LangSmith, LangFuse, LlamaIndex, RAG, multi-agent systems — matching nearly all core technical requirements
✓Proven founding engineer track record with full lifecycle ownership from architecture to production deployment at early-stage startups
✓Documented business impact: $2M in consulting revenue delivered, 4 GenAI products shipped 0→1, currently leading a 5th product
✓Open-source contributor with meaningful upstream contributions to Pipecat.ai, demonstrating depth in voice agent and real-time AI systems
✓Published AI researcher with 3 peer-reviewed papers, showing theoretical grounding and ability to think rigorously about AI systems

Key Concerns

!No code sample submitted makes it impossible to directly assess engineering quality, which is critical for a founding engineer role — this must be addressed in the interview process
!Classical ML and scientific computing skills (NumPy, SciPy) are not evidenced, and the role requires strong ML fundamentals beyond LLM API integration

Culture Fit

82%

Growth Potential

High

Salary Estimate

$90,000 - $115,000

Assessment Reasoning

FIT decision is based on the candidate meeting approximately 85% of required technical skills with direct, production-proven experience in the most critical areas of the role (agentic AI, LangGraph/LangSmith/LangFuse, RAG, multi-agent systems, LLM provider integrations, vector databases, and early-stage startup founding engineering). They have demonstrated quantified business impact ($2M in engagements, 4 products shipped), open-source contributions, published research, and team leadership — all of which align with what AlpacaRelay needs in a founding engineer. The score of 82 reflects strong overall alignment with a confidence of 78 due to the absence of a code sample, which is a non-trivial gap for a founding role. The missing skills (SciPy, Kubernetes, MCP servers, CrewAI) are relatively minor or learnable. The candidate should advance to a technical interview with a mandatory coding assessment component.

Interview Focus Areas

Deep technical dive on agentic AI system design: Have candidate walk through a specific LangGraph-based agent architecture they built, including state management, tool calling, error handling, and observability decisionsClassical ML and data fundamentals assessment: Probe knowledge of NumPy, SciPy, embeddings math, evaluation metrics, and ML fundamentals to verify depth beyond framework usageProduction incident and debugging scenario: Assess how candidate handles latency spikes, LLM failures, and RAG quality degradation in live systemsStartup culture and ownership mindset: Explore decision-making autonomy, how they handle ambiguity, and examples of taking initiative beyond assigned scopeLive coding or take-home: Given no code sample was submitted, a practical coding exercise is essential before making an offer

Code Review

FairSenior Level

No code example was submitted with this application, which is a meaningful gap for a founding engineering role where code quality is critical. However, indirect signals from the resume — including open-source contributions to Pipecat.ai, building a custom vector DB SDK, and architecting production AI systems — suggest solid engineering capability. A technical assessment or GitHub portfolio review should be a mandatory step before advancing this candidate.

+GitHub profile referenced (github.com/SwAt1563) suggesting public work exists, though not directly submitted
+Resume demonstrates architectural decision-making ability: microservices design, async/sync hybrid workflows, custom vector DB SDK — indicative of production-grade engineering thinking

-No code sample was provided for direct assessment, limiting ability to evaluate code quality, style, testing practices, and engineering rigor
-Cannot assess Python code quality, type safety, test coverage, or adherence to software engineering best practices without a submission

Experience Overview

7y total · 4y relevant

The candidate presents a compelling profile as a hands-on AI engineer with 4 years of dedicated GenAI experience, including multiple founding engineer and team lead roles at early-stage startups. Their technical stack is exceptionally well-aligned with the role's agentic AI requirements, covering LangGraph, LangSmith, LangFuse, RAG architectures, vector databases, and multi-provider LLM integration. The main gaps are around classical ML tooling (NumPy/SciPy), Kubernetes, and MCP servers, but these are addressable given their demonstrated learning velocity and depth in adjacent areas.

Matching Skills

PythonLangChainLangGraphLangSmithLangFuseLlamaIndexOpenAI APIsAnthropic APIsVector Databases (Milvus, Qdrant, Chroma, FAISS, PGVector)Retrieval-Augmented Generation (RAG)PostgreSQLDockerAWS (EC2, RDS, S3, Lambda)GitHub ActionsPrompt EngineeringMulti-Agent ArchitecturesEmbeddings and Hybrid Search

Skills to Verify

SciPyNumPy (not explicitly mentioned)CrewAIKubernetesGCP (as primary platform)MCP Servers and Tool Integrations (not explicitly mentioned)Formal evaluation frameworks (LLM evals infrastructure)

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