[5 career paths, 5 projects]

[5 career paths, 5 projects] 5 CODING projects That Will Get You Hired Scalable Microservices E- Commerce Platform For Software Engineers Build an Amazon-like marketplace backend with separate microservices for products, users, orders, payments, and search. Use event streaming (Kafka) for order processing and eventual consistency. Add an admin dashboard for monitoring. Deploy with Kubernetes to demonstrate scalability. Skills demonstrated: • Distributed systems, REST/gRPC APIs, Docker & Kubernetes • Database design (SQL + NoSQL), caching (Redis) • CI/CD pipelines, cloud deployment (AWS/GCP/Azure) • Unit/integration testing & observability (Prometheus, Grafana) Custom Large Language Model Fine-Tuning Pipeline For AI Engineers Take an open-source LLM (e.g., LLaMA-2, Falcon) and fine- tune it on a specialized dataset (e.g., legal, medical, or finance domain). Build an inference API that can answer domain-specific queries reliably. Add evaluation benchmarks and latency comparisons. Skills demonstrated: • Deep learning frameworks (PyTorch/TensorFlow) • Transfer learning, parameter-efficient fine-tuning (LoRA, PEFT) • Serving models with FastAPI + GPU inference optimization • Prompt engineering & evaluation metrics End-to-End Predictive Analytics Pipeline For Data Scientist Create a pipeline that predicts customer churn for a subscription service. Pull raw user activity logs into a data lake, transform features, train ML models, and explain results with SHAP values. Deploy a dashboard that business stakeholders can use to monitor churn risks in real-time. Skills Demonstrated: • Real-time multiplayer architecture • Frontend game logic (React, Phaser.js) • Backend + database optimization • User experience design Automated ML Deployment & Monitoring System For MLOps / AI Systems Engineer Build an automated pipeline where data changes trigger retraining of a fraud detection model. Use MLflow for experiment tracking and set up drift detection alarms. Deploy with a FastAPI service that continuously updates with retrained models. Skills demonstrated: • CI/CD for ML models (MLflow, DVC) • Experiment tracking, reproducibility • Containerization (Docker), orchestration (Kubernetes) • Model monitoring & drift detection • Data versioning and retraining automation AI-Powered Recommendation Engine For Full-Stack Data Product Build a Netflix-style movie recommendation system with hybrid methods (content + collaborative filtering). Provide a web interface where users log in, rate movies, and get personalized suggestions. Deploy the backend on AWS and integrate logging for A/B testing of different algorithms. Skills demonstrated: • Recommender systems (collaborative filtering, embeddings, hybrid models) • Backend APIs (GraphQL/REST) • Frontend visualization (React/Next.js, D3.js) • Cloud deployment & scaling (AWS Lambda, serverless) • Experimentation (A/B testing)