A strong AI portfolio shows you can ship useful solutions, measure quality, and operate them responsibly. Pick 3–4 projects from below and deliver them with clean code, a live demo, clear metrics, and a short write‑up on trade‑offs and lessons learned.
- RAG Chatbot for a Real Dataset
- Build a retrieval‑augmented generation app over a domain you care about (college handbook, course notes, or a local NGO’s FAQs).
- What to show: Retrieval precision/recall, hallucination rate, p95 latency, and cost‑per‑query; tracing, chunking strategy, reranking, and freshness updates.
- Stretch: Add guardrails (output schemas, citation checks), feedback thumbs, and an admin dashboard.
- Agentic Workflow for a Bounded Task
- Create a plan‑act‑reflect agent to handle a routine workflow (e.g., ticket triage to knowledge‑base suggestions, or data QA with anomaly reports).
- What to show: Tool permissioning, audit logs, fallback/timeout logic, and human‑in‑the‑loop approval for risky actions.
- Stretch: Add evaluation prompts that score task success and safety before actions execute.
- Recommender System with Bias and Cold‑Start Handling
- Build a content‑based or hybrid recommender (courses, articles, or products) with explainable factors.
- What to show: Offline metrics (NDCG, MAP), coverage/diversity, and a simple explainer (“recommended because…”).
- Stretch: Bandit or A/B simulation, cold‑start via metadata or LLM embeddings.
- Computer Vision App on Edge or Mobile
- Implement an on‑device classifier or detector (waste sorting, plant disease, lab equipment safety) with model compression.
- What to show: Accuracy/F1, on‑device latency, model size, and battery impact; compare quantization or distillation.
- Stretch: Add privacy features (on‑device inference only) and offline mode.
- Time‑Series Forecasting with Anomaly Detection
- Forecast demand, energy use, or attendance; flag anomalies and suggest interventions.
- What to show: Metrics (sMAPE, MASE), confidence intervals, and interpretable features.
- Stretch: A simple dashboard with alerts and backtesting across multiple horizons.
- NLP Pipeline for Document Intelligence
- Build an end‑to‑end pipeline: OCR -> chunking -> classification/extraction -> verification (regex/rules) -> export to CSV.
- What to show: Precision/recall for extraction, latency, and an error taxonomy with examples and fixes.
- Stretch: Add an LLM verifier step that flags low‑confidence fields for human review.
- MLOps “Productionize This” Service
- Take any of the above models and make it production‑ready.
- What to show: Containerization, CI/CD, model registry, feature store (if applicable), monitoring (quality/drift), alerts, and rollback playbook.
- Stretch: Cost monitoring and autoscaling; canary releases with versioned evaluations.
How to present each project (checklist)
- One‑page README: Problem, users, data, solution diagram, decisions, metrics, limits, and next steps.
- Live demo: Simple UI or notebooks + API endpoint; short video walkthrough.
- Metrics first: Quality (accuracy/precision/recall or task‑success), latency, and cost; include dashboards or plots.
- Reliability and safety: Tests, input/output validation, basic red‑team cases, and logging.
- Ethics/governance: Data sources, consent/licensing, bias checks, and how users can contest errors.
90‑day roadmap to finish 3 standout projects
- Weeks 1–4: Build the RAG chatbot + eval dashboard; instrument tracing, citations, latency, and cost.
- Weeks 5–8: Ship the agentic workflow with tool scopes and audit logs; add task‑success evals and human approval.
- Weeks 9–12: Productionize one project with MLOps; add monitoring, drift alerts, and rollback; publish a concise case study.
Signals that impress recruiters
- Domain depth: Pick real problems and real users (campus/org datasets) instead of toy corpora.
- Measurable outcomes: “Cut response time by 60%,” “Reduced hallucinations from 15% to 3%,” “Edge model 4× smaller with same F1.”
- Responsible AI: Clear data licenses, privacy posture, bias analysis, and an appeals/feedback path.
Tooling suggestions (use what you know)
- LLM/RAG/Agents: Python, LangChain/LlamaIndex, OpenAI/Anthropic or open‑weights, vector DB (FAISS/Chroma/PGVector), rerankers.
- CV/TS/NLP: PyTorch/TF, scikit‑learn, Hugging Face, Ultralytics, Prophet/darts.
- MLOps: Docker, FastAPI, GitHub Actions, MLflow/W&B, Prometheus/Grafana, Great Expectations, pytest.
Bottom line: Don’t just “build a model”—ship small, useful systems with evaluation, reliability, and ethics baked in. Three well‑executed projects with live demos and clear metrics will outweigh ten toy notebooks and make your portfolio stand out.