ITLearning

AI Engineer Training Curriculum

AI Engineer Training Programs: From Zero to Job-Ready

AI Engineer Training Programs

From Zero to Job-Ready in 6–9 Months — hands-on, interview-ready projects throughout.

🌍 Global AI Job Market Overview (2025)

500K+

Estimated AI/ML engineering roles globally

62.8%

AI roles offering hybrid/remote options

29.4%

Share of AI postings concentrated in the U.S.

1.8%

US postings explicitly requiring AI skills

🎯 PROGRAM 1: JUNIOR AI ENGINEER 6–7 months

Target: Entry-level roles listed as “0–2 years.” Focus on reproducible code, clear metrics, and business outcomes.

Phase 1: Foundation & Programming (8 weeks)

Course 1.1 — Python Programming for AI 3 weeks

Learning Objectives:

  • Python syntax, data structures, OOP patterns
  • Core libs: NumPy, pandas, matplotlib, seaborn
  • Version control with Git & GitHub (branches, PRs)
🚀 Hands-on Projects
  1. Stock Price Analyzer — fetch, clean, and visualize OHLCV data; moving averages & simple signals
  2. Personal Finance Dashboard — monthly expense categorization with charts & export
  3. Weather Data ETL — ingest REST API → tidy dataset → daily CSV/Parquet outputs
💼 Interview Talking Points
  • “Built a Python ETL that automated daily data refresh and validation.”
  • “Used Git feature branches and PR reviews to manage changes.”

Course 1.2 — Mathematical Foundations 2 weeks

Learning Objectives:

  • Linear algebra (vectors, matrices), probability, statistics
  • Calculus for optimization (gradients)
  • Bias/variance, overfitting concepts
🚀 Hands-on Projects
  1. A/B Testing Framework — power analysis, p-values, confidence intervals
  2. Basic Recommender — user-item CF with cosine similarity

Course 1.3 — Data Handling & Preprocessing 3 weeks

Learning Objectives:

  • Data cleaning, encoding, imputation, feature engineering
  • SQL basics (joins, windows), pandas I/O
  • Designing simple pipelines
🚀 Hands-on Projects
  1. E-commerce ETL — incremental load from CSV → warehouse tables
  2. Customer Segmentation — k-means on RFM features
  3. Data Quality Monitor — missingness & distribution drift dashboard

Phase 2: Machine Learning Fundamentals (10 weeks)

Course 2.1 — Classical ML with scikit-learn 4 weeks

Learning Objectives:

  • Supervised vs. unsupervised learning; model selection
  • Validation, cross-val, metrics (AUC, F1, RMSE)
  • scikit-learn pipelines & grids
🚀 Hands-on Projects
  1. HR Screening Assistant — candidate fit prediction (logistic/XGBoost)
  2. Diagnosis Predictor — tabular health dataset, class imbalance handling
  3. Fraud Detector — anomaly sensitivity & cost-aware thresholds
  4. Price Optimizer — demand curve & revenue trade-offs

Course 2.2 — Deep Learning Basics 4 weeks

Learning Objectives:

  • Neural network building blocks; CNNs & RNNs
  • TensorFlow/Keras and PyTorch fundamentals
  • Intro to CV & NLP tasks
🚀 Hands-on Projects
  1. Smart Camera — face detection + simple anomaly flagging
  2. Sentiment Classifier — social comments; confusion matrix & error analysis
  3. Doc Classifier — multi-label taxonomy
  4. Image Enhancer — super-resolution on small set (SSR baseline)

Course 2.3 — Model Serving & Lite MLOps 2 weeks

Learning Objectives:

  • FastAPI for inference services
  • Docker packaging
  • Basic deployment (Heroku/Render/AWS Lambda)
🚀 Hands-on Projects
  1. AI API — expose two models via REST; JSON schemas & tests
  2. Monitoring Panel — latency & accuracy trend tiles

Phase 3: Specialization & Portfolio (6 weeks)

Course 3.1 — Industry Application (choose one) 3 weeks

Option A: Computer Vision
  • Retail Analytics — footfall & dwell-time from video
  • Quality Control — defect detection on product images
Option B: Natural Language Processing
  • Support Chatbot — FAQ intents + fallback
  • Content Moderation — toxicity & PII flags
Option C: Recommenders
  • Personalized Learning — next-lesson ranker
  • Media Recs — top-N with implicit feedback

Course 3.2 — Junior Capstone 3 weeks

Choose one end-to-end solution:

🎯 Capstone Options
  1. Smart City Traffic Optimizer — demand forecasting + signal timing simulation
  2. Agricultural Yield Predictor — satellite + weather features; SHAP explainability
  3. Mental Health Assistant — empathetic intent routing (safety filters; resources handoff)
  4. Supply Chain Optimizer — stock-out forecasting & reorder policy

🎯 PROGRAM 2: INTERMEDIATE AI ENGINEER 8–9 months

Target: Mid-level roles (often listed as “2–4 years”). Emphasis on scalable systems, GenAI, and production MLOps.

Phase 1: Advanced Foundations (6 weeks)

Course 1.1 — Advanced Programming & Architecture 3 weeks

Learning Objectives:

  • Design patterns for AI services; dependency injection
  • Microservices architecture; async workers & queues
  • Performance profiling & vectorized Python
🚀 Hands-on Projects
  1. Scalable ML Platform — gateway + model workers + load balancer
  2. Real-time Streaming — Kafka ingestion → online features

Course 1.2 — Advanced Math & Optimization 3 weeks

Learning Objectives:

  • Bayesian inference; MAP/MCMC intuition
  • Hyperparameter optimization: Bayesian + early stopping
  • Information theory for representation learning
🚀 Hands-on Projects
  1. Auto-Tuning Framework — orchestrate HPO across models
  2. Bayesian A/B — Thompson sampling dashboard

Phase 2: Advanced ML & Generative AI (12 weeks)

Course 2.1 — Advanced Deep Learning 4 weeks

Learning Objectives:

  • Transformer architectures & attention
  • VAEs, GANs; representation learning
  • Advanced CV/NLP pipelines
🚀 Hands-on Projects
  1. AI Content Generator — controlled text/image generation (safety filters)
  2. Synthetic Data Factory — GANs for privacy-preserving training sets
  3. Multimodal Assistant — fuse text + image prompts
  4. Advanced OCR — transformer-based document understanding

Course 2.2 — LLMs & Retrieval-Augmented Generation (RAG) 4 weeks

Learning Objectives:

  • Prompt engineering; function/tool use
  • Fine-tuning & adapters (LoRA/PEFT)
  • Vector databases; embedding evaluation
🚀 Hands-on Projects
  1. Enterprise Knowledge Assistant — RAG over docs with evaluations
  2. Code Assistant — constrained generation with tests
  3. Multi-lingual Translator — streaming translations with QoS
  4. Research Summarizer — long-context chunking + citation checks

Course 2.3 — MLOps & Production Systems 4 weeks

Learning Objectives:

  • CI/CD for ML (MLflow, DVC), containers, IaC
  • Kubernetes; autoscaling; blue/green & canary
  • Monitoring: drift, performance, cost; model cards
🚀 Hands-on Projects
  1. Model Lifecycle Platform — train → register → serve → monitor
  2. Multi-Model Inference — routing & autoscaling across models
  3. Observability Suite — metrics, traces, alerts for ML endpoints

Phase 3: Advanced Specialization (10 weeks)

Course 3.1 — Choose Specialization 6 weeks

Option A: Computer Vision
  • Instance/Segmentation — detect + segment products on conveyor
  • Re-ID/Tracking — multi-camera association for logistics
Option B: NLP & LLMOps
  • Evaluation Harness — rubric-based and statistical evals
  • Safety & Guardrails — content and PII filters; audit logs
Option C: Recommenders & Decisioning
  • Bandits/Uplift — treatment optimization for promos
  • Real-time Features — online/offline consistency with a feature store

Course 3.2 — Intermediate Capstone & Interview Pack 4 weeks

Deliver a production-grade system with documentation, cost tracking, and an executive readout.

🎯 Capstone Examples
  1. GenAI Knowledge Assistant @ Scale — multi-tenant RAG with evaluations & guardrails
  2. Vision-Driven QC — real-time defect detection on edge devices with central monitoring
  3. Streaming Recommender — bandit-based ranker with online metrics & budget controls
💼 Deliverables
  • GitHub repos + CI pipelines + infra as code
  • Metrics dashboard (latency, accuracy, drift, cost)
  • 5-min demo video and stakeholder slide deck

Skills Map ↔ Typical Job Requirements

Junior Must-Haves

  • Python (pandas, NumPy), Git/GitHub
  • EDA, data cleaning, feature engineering
  • SQL (joins, window functions)
  • Classical ML, basic DL (Keras/PyTorch)
  • Model serving (FastAPI), Docker basics
  • Reporting & storytelling to stakeholders

Intermediate Must-Haves

  • Tree-based models, tuning, SHAP
  • Transformers, GenAI (RAG, eval)
  • MLOps (MLflow/DVC), CI/CD, Kubernetes
  • Monitoring (drift, latency, cost)
  • Cloud (AWS/GCP/Azure) deployment
  • Experiment design & A/B testing

Nice-to-Haves

  • Feature stores (Feast/Tecton concepts)
  • Streaming (Kafka), vector DBs
  • Privacy, security, governance
  • Domain expertise (FinTech, Health, Retail)

Ready to build an interview-proof AI portfolio?

Weekly 1:1 mentor sessions Code reviews Mock interviews Portfolio & resume support
Start Junior Program Start Intermediate Program