AI Research Engineer (Early Career)
1.5y relevant experience
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Executive Summary
The candidate is an exceptionally strong match for this role — one of the clearest skill-to-job alignments possible at the entry level. A 2026 Cum Laude CS graduate with 1.5 years of professional AI/ML engineering experience, the candidate has already shipped production LangGraph agentic workflows, cited RAG over pgvector, LLM failover gateways, and evaluation pipelines that gate CI/CD deploys — exactly the stack and engineering culture Pergola Studio is looking for. The resume goes beyond checkbox matching: the candidate demonstrates the evaluation-first, cost-aware, production-oriented thinking that most new grads lack, backed by a competitive track record of hackathon wins. The only meaningful gaps are the absence of Rust experience (qualified as a 'plus' in the description) and a timezone question for the Philippines-based candidate. This candidate should be moved to interview immediately.
Top Strengths
- ✓Production-shipped LangGraph agentic workflows and pgvector RAG pipelines — directly maps to core job responsibilities
- ✓Evaluation-first engineering mindset: eval suites gating deploys, per-node cost/latency tracking — rare at this career stage
- ✓Award-winning competitive record across multiple hackathons and innovation competitions, demonstrating initiative and ability to build real things fast
- ✓Full-stack ML ownership: from synthetic data generation and fine-tuning (QLoRA/DoRA/DPO) through deployment and monitoring
- ✓Multi-language engineering breadth (Python, Go, TypeScript, Java, Kotlin, C#) with solid systems exposure in Go — strong foundation for the 'not just notebooks' requirement
Key Concerns
- !No Rust experience — listed as a required skill in the job posting (though described as a 'plus' in the requirements narrative); worth probing
- !Philippines-based candidate applying for EU/US timezone role — overlap feasibility should be confirmed early in the process
Culture Fit
Growth Potential
High
Salary Estimate
$45,000 - $60,000 (entry-level range; Philippines-based cost-of-living context may inform negotiation)
Assessment Reasoning
FIT decision is straightforward. The candidate meets or exceeds every substantive requirement for this role: CS degree (Cum Laude, strong university), Python proficiency, LangGraph, RAG (pgvector), AI agents, LLM fundamentals (fine-tuning, prompting, evaluation), production-quality engineering practices, and demonstrated hands-on projects. The required skills list includes Rust, Java, LangGraph, vector databases, and prompt engineering — the candidate has Java, LangGraph, pgvector/ChromaDB, and clear prompt/eval engineering experience; only Rust is absent, and the job description itself frames systems-language exposure as 'a plus.' Experience level at 1.5 years fits squarely in the 0-2 year target band. The candidate's profile reads as better-prepared for this specific role than most entry-level applicants would be, with production deployment and evaluation experience that the job description explicitly values ('not just notebooks'). Timezone feasibility is the only operational concern worth confirming, and it is not a disqualifying factor.
Interview Focus Areas
Experience Overview
1.5y total · 1.5y relevantThe candidate is a Cum Laude CS graduate (2026) with 1.5 years of directly relevant professional AI/ML engineering experience — building production agentic workflows, RAG pipelines, and LLM integrations at Eden and WizyVision. The resume reads like a near-perfect match for this role's stated requirements: LangGraph, pgvector, eval suites, cost tracking, and fine-tuning are all present in production context. The only notable gap versus the required skills list is Rust, which the job description qualifies as a 'plus' rather than a hard requirement.
Matching Skills
Skills to Verify