Career Blueprint

Credit Manager→AI Engineer

⏱ 13 months•4 years experience•1 current skills

🧱Phase 1

Foundation

4 months

Skills to Develop

Python ML stack (PyTorch, Hugging Face, LangChain)Neural network architectures (transformers, CNNs, RNNs)Prompt engineering & LLM application designVector databases & RAG pipelinesML experiment tracking (W&B, MLflow)

Resources

🎓 fast.ai Practical Deep Learning (free)
🎓 Andrej Karpathy's Neural Networks: Zero to Hero
🔨 Build a RAG application with real documents
🎓 Hugging Face course (free)

🏁 Milestone

Build and deploy an AI application that solves a real problem using modern LLM/ML techniques

⚡Phase 2

Execution

5 months

Skills to Develop

Fine-tuning & RLHF techniquesML infrastructure (GPU clusters, model serving)Evaluation frameworks & benchmarkingResponsible AI & bias mitigationCost optimization for inference at scale

Resources

🔨 Fine-tune a model on custom data with evaluation
🔨 Deploy a model with <100ms latency at scale
🎓 Stanford CS224N or CS231N (free lectures)
🔨 Papers With Code — implement 3+ recent papers

🏁 Milestone

Ship an AI feature to production that handles real users with monitoring, guardrails, and cost controls

👑Phase 3

Authority

4 months

Skills to Develop

AI strategy & build-vs-buy decisionsResearch-to-production pipeline ownershipAI safety & governance frameworksTechnical leadership in fast-moving AI landscapePublishing & conference presentations

Resources

🔨 Publish a technical blog on AI engineering
👥 Present at NeurIPS, ICML, or industry AI events
🔨 Lead an org-wide AI adoption initiative
👥 Contribute to open-source AI projects

🏁 Milestone

You're the AI authority — leadership trusts your judgment on where AI can (and can't) create value

🎯

Ready to prove it?

Take a hyper-realistic mock interview tailored to this exact roadmap. Get a brutally honest STAR-method evaluation and a custom readiness score — the kind of feedback a hiring manager thinks but never says.