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AI Academy
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Skill-Gap Audit

Where you are vs. where you're heading.

We compared your CV against the AI Solutions Architect role benchmark across 8 dimensions. Strengths are clear — and so is the next 90 days.

Re-upload CV Update roadmap →

Career readiness score

Composite signal across 8 dimensions, weighted to your target role.

Up 8 pts this month
72
of 100
Foundations 88
Practical engineering 76
Production & MLOps 54
Interview signal 61

Skill radar

You · Target role

Foundations Engineering MLOps Interview Domain

Skill matrix — you vs. target

22 competencies extracted from your CV.

MLOps · CI/CD for models
45 / 85
Vector databases & retrieval
55 / 80
LLM evaluation & observability
50 / 78
Distributed training (DDP / FSDP)
40 / 75
Cost & latency optimisation
48 / 72
System design under load
60 / 82

Strengths to lead with

5 signals
✓ Strong systems-engineering background — translates well to AI architecture roles.
✓ 2 production ML projects shipped — rare among candidates we see.
✓ Cloud foundation is solid (AWS, some GCP).
✓ Communication and stakeholder framing reads at senior level.

Areas to close

6 gaps
! MLOps — limited evidence of CI/CD for models or model registries.
! Evaluation rigour — interview signal here is the biggest score lift available.
! Vector DBs & retrieval — needed for nearly every architect interview.
! Distributed training — DDP / FSDP fluency.

Confidence indicators

High

Audit confidence is 87% — built from your CV, two assessments and LinkedIn import. To push it above 95%, add a GitHub link and complete the practical engineering quiz.

90-Day Plan

Your AI-generated improvement plan

Built specifically for AI Solutions Architect · 7–10 hrs per week.

Add to curriculum →
30
Foundation lift
W 1–2 Close the eval gap
  • Complete LLM evaluation patterns course (1h 40m).
  • Read RAGAS + DeepEval docs, run on 1 of your projects.
  • Write a 1-pager: "How I'd evaluate X" — share with mentor.
W 3–4 RAG architecture
  • RAG deep-dive course modules 1–4.
  • Build a hybrid retrieval pipeline (BM25 + vector).
  • Ship to GitHub with a clean README.
60
Production muscle
W 5–6 MLOps in earnest
  • MLOps for AI Architects course (5h 10m).
  • Set up a model CI/CD pipeline on your RAG project.
  • Add a model registry + drift alerts.
W 7–8 Distributed & scale
  • DDP / FSDP fundamentals — implement on toy model.
  • Cost & latency lab: optimise a real inference path.
  • Second mentor 1:1 — architecture review of your project.
90
Interview-ready
W 9–10 System design
  • 3 architect-level system design drills (mentor-graded).
  • Build polished portfolio writeup of RAG + MLOps work.
  • Refine resume with your mentor.
W 11–12 Live runs
  • 2 mock interviews with senior practitioners.
  • AI interview simulator — behavioural + technical.
  • Apply to first 5 target roles · we'll handle warm intros.
CV & Profile

Keep your audit fresh.

Re-upload your CV any time. We re-parse, re-score and refresh your 90-day plan in under a minute. You can also connect LinkedIn or GitHub for richer signal.

Currently on file: satya_cv_v3.pdf Replace