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Evaluation: Acme AI -- Senior AI Engineer

Date: 2026-04-01 Archetype: AI Platform / LLMOps Engineer Score: 4.2/5 URL: https://jobs.example.com/acme-ai-senior-engineer PDF: output/cv-candidate-acme-ai-2026-04-01.pdf

A) Role Summary

Field Value
Archetype AI Platform / LLMOps Engineer
Domain Platform / Infrastructure
Function Build
Seniority Senior (IC4-IC5)
Remote Full remote (US timezone overlap)
Team size ~8 engineers
TL;DR Senior AI eng to build and scale LLM infrastructure for enterprise customers

B) CV Match

JD Requirement CV Match Source
"Production LLM systems" Built real-time fraud detection + LLM eval toolkit cv.md: TechFin Corp
"Model monitoring and observability" Drift detection, Grafana dashboards, retraining triggers cv.md: ML Platform Lead
"Python + distributed systems" Python, Kafka, Kubernetes, Redis cv.md: Skills
"CI/CD for ML" Reduced deploy from 2 weeks to 4 hours cv.md: TechFin Corp

Gaps

Gap Severity Mitigation
"LLM-specific experience" Medium LLM Eval Toolkit is direct proof. Frame fraud detection as "production ML → production LLM" progression
"Prompt engineering" Low Mention eval toolkit's prompt testing capabilities

C) Level and Strategy

Detected level: Senior (IC4) Candidate's natural level: Senior-Staff boundary

"Sell senior" plan: Lead with platform ownership at TechFin ("led 3-person team, built MLOps for 4 teams"). Frame as ready for Staff scope.

D) Comp and Demand

Data Point Value Source
Base salary range $180-220K Levels.fyi, similar AI infra roles
Total comp (with equity) $250-320K Glassdoor estimates
Demand trend High -- LLM infra is top-5 most in-demand LinkedIn job trends

E) Personalization Plan

# Section Current Proposed Change Why
1 Summary "Full-stack AI engineer" "AI platform engineer focused on LLM infrastructure and observability" Match JD language
2 TechFin bullets Generic ML platform Add "LLM serving" context JD specifically mentions LLMs
3 Projects Both listed equally Lead with LLM Eval Toolkit Direct LLM experience proof

F) Interview Plan

# JD Requirement STAR Story S T A R
1 Production LLM systems FraudShield scaling 10K TPS requirement Built streaming pipeline Kafka + ensemble + feature store 99.7% precision, $2M saved
2 Team leadership ML Platform team 4 teams needed MLOps Led 3-eng team, built platform Registry + A/B + feature store Deploy time 2 weeks → 4 hours

Recommended case study: LLM Eval Toolkit -- shows LLM-specific expertise + open source impact

Keywords Extracted

LLM infrastructure, model serving, observability, ML platform, distributed systems, Python, Kubernetes, model monitoring, CI/CD, prompt engineering, evaluation, production ML, enterprise AI, scalability, reliability