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