AI Engineering Masterclass

Month 0: Machine Learning & Mathematics Foundations

Goal: Build the foundational mathematics, programming, and data manipulation skills required for advanced AI engineering.

Week 1: Python & Data Foundations

NumPy and Pandas fundamentals

Vectorization and matrix operations

Data wrangling and manipulation

Hands-on data preparation mini-lab

Week 2: Machine Learning Core Concepts

Supervised and unsupervised learning

Regression and classification

Loss functions

Overfitting and underfitting

Week 3: Mathematics for Machine Learning

Linear algebra and matrix decompositions

Multivariable calculus

Gradients and partial derivatives

Probability fundamentals

Week 4: Capstone Starter Project

Build an end-to-end baseline ML pipeline using real-world datasets.

Develop data cleaning, feature extraction, and model training workflows.

Package the model as a command-line tool.

Prepare a technical architecture presentation explaining data leakage prevention and metric trade-offs.

Hands-On Project: End-to-End Baseline Predictive Pipeline

Build a complete predictive modeling pipeline using Pandas and Scikit-Learn, package the model into a usable tool, and document the architectural decisions behind the solution.

Month 1: Advanced Data Structures & Algorithms

Goal: Develop strong problem-solving skills and write clean, efficient code for competitive technical interviews.

Week 1: Arrays, Strings & Problem-Solving Patterns

Big-O analysis and hash maps

Two pointers and sliding window

Prefix sums and binary search

15–20 LeetCode Medium/Hard practice problems

Week 2: Linked Lists, Stacks, Queues & Heaps

Monotonic stacks and circular queues

Priority queues and binary heaps

Mini-project: Thread-safe LRU cache with O(1) eviction

Week 3: Trees, Graphs & Knowledge Networks

Binary trees and tries

DFS, BFS, and topological sorting

Union-Find and shortest-path algorithms

Mini-project: Real-time trie autocomplete engine

Week 4: Dynamic Programming & Advanced Algorithms

Memoization and tabulation

Backtracking and greedy algorithms

Interval-based problems

Timed mock interviews

Production Project: AI-Powered Coding Challenge Platform

Build an automated coding evaluation microservice that runs code in Docker sandboxes, evaluates algorithmic efficiency, and uses LLMs to provide step-by-step feedback and complexity optimizations. Implement persistence using PostgreSQL and asynchronous APIs using FastAPI.

Technologies: Python, FastAPI, PostgreSQL, OpenAI API, Docker, GitHub Actions.

Month 2: System Design & AI System Architecture

Goal: Learn to design scalable distributed systems and modern AI infrastructure.

Week 1: Distributed Systems Fundamentals

Scalability and load balancing

Content delivery networks and caching

CAP theorem and consistency models

Replication strategies

Lab: High-throughput URL shortener

Week 2: Data Systems & Streaming Infrastructure

SQL versus NoSQL

Redis and Elasticsearch

Kafka event streaming

Docker and Kubernetes fundamentals

Lab: Real-time event pipeline

Week 3: AI System Design Architecture

ChatGPT and Copilot architectures

Vector databases and feature stores

Embeddings and retrieval pipelines

Model serving

Week 4: Machine Learning Systems in Production

Training pipelines

Batch versus online inference

Model registries

A/B testing and canary deployments

Architectural Project: Production AI System Design Portfolio

Design an enterprise-grade fraud detection and Copilot platform for large-scale usage. Produce high-level and low-level design documents, API schemas, Kafka event mappings, vector database partitioning strategies, latency SLAs, and multi-region cost calculations.

Month 3: Leadership, Behavioral & Career Engineering

Goal: Develop executive presence, communicate technical impact, and prepare for senior engineering interviews.

Week 1: Resume & Professional Branding

Resume optimization

LinkedIn positioning

Public GitHub portfolio development

Technical blogging

Week 2: Behavioral Interview Preparation

STAR framework

Conflict resolution

Cross-functional leadership

Influencing without authority

Week 3: Executive Communication

Whiteboard storytelling

Defending architecture decisions

Explaining technical trade-offs

Communicating under pressure

Week 4: Career Strategy & Negotiation

Recruiter outreach and networking

Offer evaluation

Compensation negotiation

Career positioning

Month 4: Machine Learning & Deep Learning Foundations

Goal: Build a strong ML foundation, train classical models, and develop neural network pipelines using PyTorch.

Week 1: Classical Machine Learning

XGBoost and LightGBM

Random forests

Cross-validation

ROC-AUC and precision/recall optimization

Week 2: Feature Engineering

Feature scaling and dimensionality reduction

PCA and missing-data handling

Synthetic feature generation

Feature stores

Week 3: Deep Learning Fundamentals

Neural networks and backpropagation

Optimization algorithms

CNNs and LSTMs

Attention mechanisms

Week 4: PyTorch Mastery

Tensor operations and custom DataLoaders

Transfer learning

Experiment tracking with MLflow

Production Project: Enterprise Real-Time Fraud Detection Platform

Develop an end-to-end ML service combining XGBoost and PyTorch neural networks for low-latency transaction fraud scoring. Build an automated feature engineering pipeline, model drift monitoring dashboard, dynamic thresholding, and live REST inference endpoints.

Technologies: PyTorch, XGBoost, Scikit-Learn, MLflow, FastAPI.

Month 5: LLM Engineering & Generative AI

Goal: Build enterprise-grade LLM applications, RAG pipelines, and production-ready AI workflows.

Week 1: Foundation Models

Transformer architecture

Tokenization and embeddings

Open-source versus closed-source models

Week 2: Prompt Engineering & Tool Calling

Reasoning patterns

Structured JSON outputs

Function calling

Prompt evaluation and security

Week 3: Retrieval-Augmented Generation (RAG)

Vector databases: Pinecone, Weaviate, and ChromaDB

Hybrid search

Document chunking strategies

Knowledge graphs

Week 4: Production AI Applications

Model Context Protocol (MCP)

Guardrails and safety

Cost optimization

Memory systems

Production Project: Multimodal Enterprise RAG Knowledge Assistant

Build a production RAG platform that indexes large collections of unstructured documents, enterprise PDFs, and codebases. Implement hybrid vector and keyword search, metadata filtering, conversational memory, citation-backed responses, and guardrails to reduce hallucinations.

Technologies: OpenAI API, Claude API, LangChain, LlamaIndex, Pinecone, ChromaDB.

Month 6: Production AI Engineering & MLOps

Goal: Deploy, monitor, scale, and optimize AI models using modern DevOps and MLOps practices.

Week 1: API Development & Containerization

FastAPI and asynchronous endpoints

Docker and Docker Compose

Background workers

Week 2: MLOps & CI/CD

GitHub Actions

MLflow Model Registry

DVC

Automated regression testing

Week 3: Observability & Monitoring

Model drift detection

LangSmith prompt tracing

Prometheus metrics

Grafana alerting

Week 4: High-Performance Inference

Model quantization

vLLM and Triton Inference Server

GPU optimization

Caching strategies

Production Project: High-Throughput AI Inference Engine

Deploy an optimized LLM and ML model-serving cluster using Triton and vLLM on Kubernetes. Build CI/CD regression testing, real-time throughput telemetry, error-rate alerts, and automated model drift monitoring dashboards.

Technologies: Docker, Kubernetes, MLflow, vLLM, Triton, Prometheus, Grafana.

Month 7: AI System Design & Enterprise Architecture

Goal: Develop architectural decision-making skills for large-scale enterprise AI infrastructure.

Week 1: Intelligent Product Design

Architecture analysis of Cursor, Copilot, and Perplexity

Enterprise search architectures

Week 2: Data & Compute Infrastructure

Kafka event streaming

Distributed feature stores

Batch versus real-time processing

Week 3: Security & Responsible AI

Personally identifiable information (PII) protection

AI governance and compliance

Auditability

Human-in-the-loop design

Week 4: Enterprise Architecture Defense

Team-based high-level and low-level design

Capacity planning

Infrastructure and cost analysis

Production Project: Enterprise AI Platform Blueprint & Security Defense

Architect a multi-tenant AI platform incorporating role-based access control, PII anonymization, dynamic model routing, capacity expansion strategies, and compliance documentation for SOC 2 and GDPR requirements.

Track 1: Machine Learning Engineering (Months 8–10)

Career Paths: ML Engineer, Applied Scientist, AI Infrastructure Engineer, Recommendation Systems Engineer.

Month 8: Advanced ML Systems & Scalable Infrastructure

Week 1: Distributed training, data and model parallelism, PyTorch Lightning, Horovod, Ray, and GPU cluster allocation.

Week 2: Recommendation architectures, collaborative filtering, matrix factorization, Deep & Cross Networks, and two-tower embeddings.

Week 3: Learning to Rank, LambdaMART, vector retrieval, reranking models, and real-time feature store queries.

Week 4: A/B testing, shadow deployments, multi-armed bandits, canary releases, and feature flags.

Month 9: Production ML Optimization & Low-Latency Inference

Week 1: TensorRT, ONNX Runtime, FP16/INT8 quantization, and knowledge distillation.

Week 2: Triton Inference Server, dynamic batching, asynchronous request queuing, and shared-memory optimization.

Week 3: Data distribution shift, statistical drift testing, and concept drift mitigation.

Week 4: Batch and streaming inference architectures, Spark/Flink integration, and Redis caching.

Month 10: Enterprise Capstone Execution

Build an enterprise movie recommendation and search-ranking engine using two-tower deep learning models, TensorRT, and Triton. Support real-time personalization and low-latency ranking across 10 million items, with automated CI/CD and drift monitoring.

Capstone Deliverables: Production code, PyTorch models, ONNX deployment artifacts, Triton configuration files, benchmark report, and executive architecture deck.

Track 2: Agentic AI Engineering (Months 8–10)

Career Paths: Autonomous Systems Engineer, AI Agent Developer, Generative AI Specialist, LLM Architect.

Month 8: Building Autonomous & Intelligent Agents

Week 1: ReAct and Plan-and-Solve reasoning patterns, stateful memory, self-correction, and structured output schemas.

Week 2: LangGraph, CrewAI, OpenAI Agents SDK, and AutoGen.

Week 3: Multi-step workflow automation, external tool orchestration, database querying, and human-in-the-loop review.

Week 4: Agent evaluation, hallucination benchmarking, tool validation, prompt injection defense, and reliability scoring.

Month 9: Multi-Agent Systems & Swarm Intelligence

Week 1: Planner-executor architectures, router patterns, supervisor-worker hierarchies, and agent communication.

Week 2: Asynchronous messaging, shared-state synchronization, and conflict resolution.

Week 3: Episodic memory, semantic retrieval, context routing, and automatic conversation compression.

Week 4: Session management, error recovery, rate-limit handling, and token cost control.

Month 10: Enterprise Capstone Execution

Build an autonomous AI software development team with Planner, Coder, Code Reviewer, and QA Automation agents. The system will write, test, debug, and deploy software repositories from natural-language product requirements.

Capstone Deliverables: A functioning LangGraph engine, multi-agent sandbox execution container, evaluation benchmark report, and interactive web dashboard.

Track 3: Forward Deployed Engineering (Months 8–10)

Career Paths: Forward Deployed AI Engineer, Enterprise AI Solutions Architect, Technical AI Consultant.

Month 8: Customer Discovery & Technical Solution Architecture

Week 1: Enterprise scoping workshops, business process mapping, AI feasibility assessments, and data availability audits.

Week 2: Success KPIs, latency-versus-cost analysis, legacy system integration, and threat modeling.

Week 3: Product requirements documents, high-level architecture, interface contracts, and customer SLAs.

Week 4: Simulated customer discovery sessions with enterprise stakeholders and technical directors.

Month 9: Building Enterprise Solutions & Client Integrations

Week 1: Rapid AI MVP prototyping, customer mockups, and middleware development.

Week 2: Integration with enterprise ERPs, Salesforce/SAP, SQL databases, and internal APIs.

Week 3: On-premise and hybrid deployments, air-gapped environments, VPC configuration, and OAuth/SAML identity management.

Week 4: User acceptance testing, stakeholder feedback, UI/UX refinement, and domain feedback pipelines.

Month 10: Enterprise Capstone Execution

Develop and deploy an enterprise customer intelligence and workflow engine for a simulated Fortune 500 organization. Integrate enterprise data ingestion, security and compliance verification, customized RAG pipelines, and automated ROI dashboards.

Capstone Deliverables: Discovery report, production MVP integration code, VPC deployment scripts, end-user training documentation, and ROI evaluation deck.

Track 4: AI Architecture (Months 8–10)

Career Paths: Staff AI Engineer, Principal Engineer, Lead AI Architect, Head of AI Platform.

Month 8: Enterprise AI Platforms & Multi-Model Ecosystems

Week 1: Multi-tenant isolation, shared GPU memory pooling, centralized model registries, and global rate limiting.

Week 2: Dynamic model routing, cost/latency trade-offs, fallback strategies, semantic caching, and multi-vendor load balancing.

Week 3: Global vector databases, distributed feature stores, graph-enhanced retrieval, and data lake integration.

Week 4: Enterprise AI security policies, compliance auditing, zero-data-retention enforcement, and cost allocation.

Month 9: Scaling Enterprise AI & Infrastructure Resilience

Week 1: Multi-region cloud topologies, cross-region failover, geo-distributed vector indices, and latency optimization.

Week 2: Kubernetes for AI, custom resource definitions, GPU autoscaling, and spot instance optimization.

Week 3: Disaster recovery, fault-injection testing, automated failover, and data recovery.

Week 4: Infrastructure as Code with Terraform, internal developer portals, and automated policy guardrails.

Month 10: Enterprise Capstone Execution

Architect and implement a multi-region global enterprise AI gateway that manages multi-tenant traffic, dynamically routes LLM requests, uses intelligent vector caching, scales GPU clusters, and evaluates access policies at very low latency.

Capstone Deliverables: High-level and low-level design documents, Terraform deployment scripts, multi-region Kubernetes manifests, security assessment report, and executive architecture defense presentation.

Put your engineering skills to the test in a final design review modeled after technical reviews and hiring committee evaluations at leading technology companies.

Every student presents their Month 10 capstone project to a panel of experienced AI leaders and hiring managers in a 90-minute live defense. The review evaluates your ability to explain architectural decisions, demonstrate a working solution, reason about trade-offs, and communicate engineering decisions clearly.

The 90-Minute Design Review

Product Vision (10 minutes): Explain the customer problem, success metrics, and product constraints.

Architecture Walkthrough (20 minutes): Present the high-level and low-level designs, data flows, infrastructure, security, and capacity planning.

Live Technical Deep Dive (25 minutes): Defend architectural trade-offs, including vector databases versus search and latency versus cost.

Live Demonstration (20 minutes): Demonstrate the working deployment, monitoring dashboards, and fault-tolerance mechanisms.

Hiring Committee Review (15 minutes): Receive actionable feedback across six core engineering competencies.