Production RAG Pipeline
Intermediate Proves: RAG + Vector DBLangChain / Pinecone / FastAPI / Docker
Build an end-to-end retrieval system for querying documents with LLMs.
AI Engineer Portfolio Projects
20+ AI Engineer Portfolio Projects for 2026
RAG pipelines, fine-tuned models, deployed APIs, MLOps and AI agents — projects that prove you can ship AI in production.
Fine-tuned Sentiment Classifier
Intermediate Proves: Model Fine-tuningHuggingFace / LoRA / PyTorch
Fine-tune a transformer model for domain-specific sentiment analysis.
ML Model CI/CD Pipeline
Advanced Proves: MLOps AutomationMLflow / GitHub Actions / Docker / Railway
Automate the full ML lifecycle from training to deployment.
LLM Evaluation Framework
Advanced Proves: LLM Quality TestingPython / RAGAS / LangChain
Systematically benchmark and compare LLM and RAG pipelines.
Real-time Object Detection API
Intermediate Proves: CV DeploymentYOLOv8 / FastAPI / Redis / Docker
Build a production-grade computer vision inference API.
Multi-Agent Research System
Advanced Proves: AI AgentsLangChain / Tavily / Redis
Orchestrate autonomous agents that plan, research, and write.
LLM-powered Data Extraction Pipeline
Intermediate Proves: Structured LLM OutputOpenAI / Pydantic / FastAPI
Extract validated structured data from unstructured text.
Model Monitoring Dashboard
Advanced Proves: Production ML MonitoringEvidently AI / Grafana / PostgreSQL
Track drift, performance and health of deployed models.
Semantic Search Engine
Intermediate Proves: Vector SearchOpenAI Embeddings / Qdrant / FastAPI
Ship a low-latency embedding-based search service.
Multimodal Document Analyzer
Advanced Proves: Multimodal AIGPT-4V / LangChain / FastAPI
Parse scanned documents and images into structured records.
Instruction-tuned LLM from Scratch
Advanced Proves: LLM TrainingPyTorch / HuggingFace / QLoRA
Fine-tune a 7B model on a custom instruction dataset.
Feature Store Implementation
Advanced Proves: MLOps ArchitectureFeast / Redis / PostgreSQL
Design online + offline feature serving for ML pipelines.
AI Content Moderation API
Intermediate Proves: NLP DeploymentHuggingFace / FastAPI / Docker
Detect toxic or harmful content in real-time text streams.
Autonomous Coding Agent
Advanced Proves: AI Agents + Tool UseLangChain / Claude API / Docker / GitHub API
Build an agent that reads issues and opens working PRs.
Knowledge Graph Builder
Advanced Proves: NLP + Graph SystemsspaCy / Neo4j / LangChain
Extract entities and relationships into a queryable graph.
A/B Testing Framework for LLMs
Advanced Proves: LLM Evaluation SystemsPython / FastAPI / PostgreSQL
Run controlled experiments across prompts and models.
Speech-to-Structured-Data Pipeline
Intermediate Proves: Audio AI + Structured ExtractionWhisper / OpenAI / FastAPI
Convert spoken audio into validated structured records.
Batch Inference Pipeline
Intermediate Proves: Scalable ML SystemsPython / Celery / Redis / Docker
Process tens of thousands of inference jobs asynchronously.
LLM Guardrails System
Advanced Proves: LLM Safety & ReliabilityPython / NeMo Guardrails / FastAPI
Add safety, PII and topic guardrails around any LLM app.
Personal AI Assistant with Memory
Intermediate Proves: RAG + Long-term Memory SystemsLangChain / Mem0 / Supabase / React
Build a chat assistant with persistent personalized memory.
What AI Engineer Portfolios Need in 2026
The single biggest signal in an AI engineer portfolio is deployed models, not notebooks. Recruiters and hiring managers can spot a Jupyter-only portfolio in seconds — and it almost always loses to a candidate with one clickable, live API. Wrap your work in FastAPI, ship it in Docker, and put a live URL plus a 30-second demo GIF at the top of every README. A working endpoint beats a beautifully-formatted notebook every time.
For LLM work in 2026, prioritize RAG over fine-tuning. Most companies don't need (and can't afford) a custom fine-tune — they need retrieval over their own data with good chunking, reranking, and evaluation. Build at least two production-quality RAG projects with a real vector database (Pinecone, Qdrant, Weaviate, or pgvector), a reranker, and an evaluation harness like RAGAS. Add one LoRA or QLoRA fine-tune to prove you understand the training loop, but lead with retrieval.
You also need MLOps basics: Docker, GitHub Actions CI/CD, MLflow or Weights & Biases for experiment tracking, and at least one project showing model monitoring (drift, latency, cost). You don't need Kubernetes — a clean Docker + Railway/Fly.io deployment is enough for portfolio purposes. What matters is showing the full path from `git push` to a live endpoint, with tests and observability in between.
Finally, GitHub plus a live demo is mandatory. Every project needs a public repo with a clean README (problem, architecture diagram, tech stack, live link, demo GIF), and ideally a 2-minute Loom walkthrough. The pattern that consistently lands AI engineer interviews: 4–6 deployed projects covering RAG, an agentic workflow, MLOps, and one fine-tune — each with a live URL, a clean repo, and a one-paragraph explanation of why you chose that architecture.
Generate a personalized AI engineer project idea
Get a portfolio-ready AI project with architecture, tech stack, and a step-by-step build plan in under 60 seconds.
FAQ