Every project, with what it is assessed on

• ai-engineer

  Capstone

  Enterprise RAG Assistant

  A document-grounded AI assistant using embeddings, a vector database, retrieval, reranking, grounded generation, and source citations.

  Deliverables

  • Document ingestion pipeline
  • Pinecone, FAISS, or another vector database integration
  • RAG quality evaluation
  • Deployed demo with feedback capture

• ai-engineer

  Capstone

  LangGraph Multi-Agent Workflow

  A stateful AI workflow that routes tasks, calls tools, remembers state, handles failures, and uses human approval for risky actions.

  Deliverables

  • LangGraph architecture diagram
  • Tool-calling workflow
  • Checkpointing and logging
  • Security and guardrail tests

• ai-engineer

  Capstone

  AI Production Readiness Review

  Turn one AI application into a production-ready system with cost controls, observability, prompt-injection defences, and governance documentation.

  Deliverables

  • Threat model
  • Cost and latency dashboard
  • Evaluation suite
  • Runbook and model card

• ml-engineer

  Capstone

  Business Prediction System

  A complete classical machine learning project for churn, risk, lead scoring, customer value, price, or cost prediction.

  Deliverables

  • Exploratory analysis and data validation
  • Feature engineering workflow
  • Baseline and tuned final model
  • Metric summary and error analysis

• ml-engineer

  Capstone

  Segmentation and Anomaly Analysis

  Unsupervised learning to segment users, products, or transactions and identify unusual records worth investigation.

  Deliverables

  • Cluster profiles
  • Anomaly list with reasoning
  • Business recommendations
  • Visual summary

• ai-engineer

  Module 01

  Machine Learning Foundations for AI Engineers

  Build a classical machine learning baseline and a deep learning model for the same classification task.

  Deliverables

  • Metrics comparison
  • Error analysis
  • Reproducible training scripts

• ai-engineer

  Module 02

  LLM Foundations and Prompt Engineering

  Create a prompt-evaluated assistant for a specific business workflow.

  Deliverables

  • Prompt library
  • Evaluation cases
  • Quality scorecard
  • Failure analysis

• ai-engineer

  Module 03

  Embeddings, Vector Search, and Vector Databases

  Build a semantic search system over PDFs, documents, or internal knowledge-base data.

  Deliverables

  • Chunking strategy
  • Embedding pipeline
  • Vector index
  • Search quality evaluation

• ai-engineer

  Module 04

  Retrieval-Augmented Generation

  Build a production-style RAG chatbot that answers from a document collection with citations.

  Deliverables

  • LangChain or LlamaIndex implementation
  • Vector database integration
  • RAG evaluation report
  • Deployment demo

• ai-engineer

  Module 05

  LangChain, LangGraph, and Agentic Workflows

  Create a multi-step research or support agent using LangGraph with tool usage and human approval.

  Deliverables

  • Graph diagram
  • State schema
  • Tool definitions
  • Logs and traces
  • Safe fallback behaviour

• ai-engineer

  Module 06

  AI Application Backend and Production Deployment

  Deploy a RAG or agentic AI application with authentication, logs, and feedback collection.

  Deliverables

  • API service
  • Frontend or demo UI
  • Dockerized deployment
  • Monitoring dashboard

• ai-engineer

  Module 07

  AI Safety, Security, and Governance

  Add security and governance controls to an AI assistant.

  Deliverables

  • Threat model
  • Red-team test cases
  • Guardrails implementation
  • Audit-ready report

• ml-engineer

  Module 01

  Python, SQL, and Data Handling Foundations

  Build a clean data ingestion, validation, and exploratory analysis pipeline for a messy customer, sales, or operations dataset.

  Deliverables

  • Clean repository structure
  • Exploratory analysis notebook
  • Data validation checks
  • Unit-tested utility functions

• ml-engineer

  Module 02

  Mathematics and Statistics for Machine Learning

  Implement linear regression with gradient descent from scratch and validate the result against a library implementation.

  Deliverables

  • From-scratch implementation
  • Loss curve
  • Metric summary
  • Short explanation of convergence

• ml-engineer

  Module 03

  Data Preparation and Feature Engineering

  Create a reusable preprocessing workflow for a tabular business dataset and document every transformation.

  Deliverables

  • Preprocessing notebook
  • Feature dictionary
  • Data leakage checklist
  • Before-and-after metric summary

• ml-engineer

  Module 04

  Supervised Learning: Regression

  Predict price, cost, delivery time, or customer value using structured data.

  Deliverables

  • Baseline model
  • Tuned final model
  • MAE, RMSE, and R2 summary
  • Residual analysis

• ml-engineer

  Module 05

  Supervised Learning: Classification

  Build a churn, loan-risk, lead-scoring, fraud-risk, or support-ticket classification model.

  Deliverables

  • Clean training notebook
  • Metric table
  • Confusion matrix
  • Error-analysis notes

• ml-engineer

  Module 06

  Unsupervised Learning and Anomaly Detection

  Create customer segmentation and anomaly detection for transaction, usage, or operational data.

  Deliverables

  • Cluster profile summary
  • Silhouette and Davies-Bouldin scores
  • Two-dimensional visualization
  • Outlier investigation notes

• ml-engineer

  Module 07

  Model Evaluation, Tuning, and Interpretation

  Tune and interpret a tabular machine learning model for a real business-style decision problem.

  Deliverables

  • Cross-validation results
  • Tuning configuration
  • Final metric summary
  • Failure-case notes

• ml-engineer

  Module 08

  Portfolio, Communication, and Interview Readiness

  Polish three machine learning repositories into a job-ready portfolio with clear problem statements and results.

  Deliverables

  • Portfolio README
  • Project summary pages
  • Interview question notes
  • Resume project bullets