# 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

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## 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

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## 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
