Engineering Projects That'll Get You Hired

Across hundreds of hiring events, a small set of recurring project types consistently correlate with faster interviews and higher offer rates, spanning AI/ML systems, backend and platform work, full-stack products, data and analytics infrastructure, and DevOps or SRE style reliability projects.

Each archetype signals different skills, and candidates do not need all of them; one to three high-quality projects aligned to your target role, such as AI engineer, data engineer, or senior backend, are usually enough to stand out during a Fonzi AI Match Day.

The following subsections detail specific project ideas and how to build them so they feel indistinguishable from work done at a modern AI startup.

AI / ML Engineering Projects That Look Like Startup Work

In 2026, the most compelling AI projects combine LLMs or ML models with data pipelines, evaluation harnesses, and real users, rather than just Jupyter notebooks, and both engineering students and experienced engineers benefit from building systems that demonstrate the full lifecycle from data to deployment.

Project ideas with specifics:

1. Retrieval-Augmented Generation (RAG) System: Build a knowledge base for 5,000+ documents using OpenAI GPT-4 or Anthropic Claude, implement vector search with Pinecone or PostgreSQL pgvector, and include evaluation scripts comparing retrieval quality, latency under load, and caching strategies.

2. Personalized Content Ranking Service: Train on implicit feedback such as clicks and dwell time with an offline Python and PyTorch job, deploy an online inference endpoint in FastAPI on AWS ECS or Fly.io, and track experiments with Weights & Biases measuring precision, recall, and cost per 1,000 requests.

3. Fraud or Anomaly Detection Pipeline: Process synthetic fintech-style transactions with Kafka ingestion and Spark or Flink streaming analytics, and create a dashboard with Superset or Metabase showing real-time alerts to demonstrate complex data processing and analysis.

4. Multi-modal Classification System: Build a system processing both text and images, such as product categorization for e-commerce, using CLIP or similar models with batch and real-time inference options.

Each project should show end-to-end ownership, experiment tracking, clear metrics, and a deployment story that demonstrates understanding of running ML in production.

Document projects in a short, structured write-up covering problems, constraints, design, tradeoffs, and results, so Fonzi AI can link them directly in candidate profiles for founders to review pre-interview.

Backend & Systems Projects That Show Real System Design Ability

System design interviews at FAANG and high-growth startups are increasingly anchored in candidates’ own real projects, giving those with concrete systems they’ve built a major advantage over candidates who can only discuss theoretical solutions.

Concrete backend/system design project ideas:

1. Rate-Limited URL Shortener Service: Build using Go or Rust with PostgreSQL and Redis. Implement an API gateway with rate limiting, load test to 5–10k requests per second with k6 or Locust, and add monitoring with Prometheus/Grafana.

2. Event-Driven Order Processing System: Create a mock e-commerce platform using Kafka or AWS SNS/SQS. Build separate inventory, payment, and notification services with eventual consistency patterns. Handle failures gracefully with retry logic and dead-letter queues.

3. Feature Flag Service: Implement REST/GraphQL APIs with rollout strategies (percentage-based, user targeting). Build a simple React admin UI and deploy with Docker and Kubernetes on a small GKE or EKS cluster.

4. Distributed Task Queue: Similar to a simplified Celery or Sidekiq, with job priorities, retries, and observability. Demonstrate understanding of distributed systems patterns.

Hiring managers look for thoughtful data modeling, handling failures and retries, idempotency, observability with logs, traces, and metrics, and clear README-level documentation.

Full-Stack Product Projects That Prove You Can Ship Features End-to-End

CTOs of seed to Series B startups on value engineers who can go from vague problem to shipped feature without waiting on a big team. Full-stack projects demonstrate this ability to create complete solutions.

Realistic full-stack project ideas:

1. Experiment Tracking Tool for AI Teams: Build a lightweight MLflow-style UI using Next.js 14 with the App Router, TypeScript, Prisma, PostgreSQL, and a simple auth system, tracking metrics, comparing runs, and visualizing results over four to six weeks of development.

2. Issue Tracker with GitHub Integration: Implement GitHub OAuth and basic Kanban boards, focusing on performance with Core Web Vitals and accessibility with ARIA and keyboard navigation, demonstrating attention to the engineering design process beyond functionality.

3. Real-Time IoT Metrics Dashboard: Use WebSockets or Server-Sent Events with a Node.js or Elixir backend and a charting library like Recharts or D3 to display data from sensors or simulated devices with real-time updates.

4. Team Collaboration Tool: Build a simplified Notion or Linear clone with real-time collaboration features using CRDTs or operational transforms.

The focus should be on product polish (empty states, error handling, responsive layout, onboarding) and engineering hygiene (tests, CI, deployment scripts), not just the tech stack buzzwords.

For frontend-heavy roles, depth in complex state management, performance optimization, and design systems (e.g., Storybook, component libraries) should be highlighted within these projects.

Data Engineering & Analytics Projects That Show You Can Own the Pipeline

Data engineers and analytics engineers stand out when they demonstrate the ability to design, build, and monitor reliable pipelines rather than just write SQL, showing they can handle data at scale and generate insights.

Concrete project ideas:

1. End-to-End ELT Pipeline: Ingest a public dataset (NYC TLC taxi data or GitHub events) into a data lake (S3/MinIO). Transform with dbt and expose via BigQuery or Snowflake. Include data quality checks and documentation of your schema decisions.

2. Incremental CDC Pipeline: Set up change data capture from PostgreSQL to a warehouse using Debezium or Airbyte. Orchestrate daily jobs with Airflow or Dagster. Alert on failures via PagerDuty or Slack webhooks. This shows understanding of real production data systems.

3. Product Analytics Stack: Power a simple metrics dashboard (DAU/WAU/MAU, retention cohorts) for a mock SaaS app. Build with Looker Studio, Metabase, or Apache Superset. Demonstrate understanding of the science behind meaningful product metrics.

4. Data Quality Framework: Implement Great Expectations or similar tools with automated testing of data pipelines, anomaly detection, and alerting.

Impressive projects highlight data modeling with star or snowflake schemas, partitioning and clustering decisions, cost control strategies, and clear data quality checks.

DevOps / SRE / Platform Projects That Broadcast Reliability Mindset

Many AI and infrastructure-heavy startups in the network are short on platform engineers who understand both software and operations, and projects in this space demonstrate a reliability mindset crucial for production systems.

Concrete project ideas:

1. Multi-Service Kubernetes Deployment: Design and deploy a small application on Kubernetes with GitHub Actions CI/CD. Implement canary deployments and autoscaling (HPA based on CPU or custom metrics). Document the process and tools used.

2. Centralized Observability Stack: Build using Prometheus, Loki, Tempo, and Grafana. Instrument a sample microservice app with OpenTelemetry and create SLO dashboards showing error budgets and performance metrics.

3. Internal Developer Platform CLI: Create a scaffolding tool that generates new services with opinionated templates (Dockerfiles, health checks, monitoring configurations). Similar to what platform teams at larger organizations build to improve development velocity.

4. Infrastructure as Code Repository: Manage cloud resources with Terraform or Pulumi. Include modules for common patterns, automated testing of infrastructure changes, and cost monitoring.

These projects signal familiarity with infrastructure as code, secrets management, rollback strategies, and cost and performance tradeoffs in 2026-era cloud environments.

Include diagrams, such as architecture diagrams created with Excalidraw or Lucidchart, in the project README to make the design easy to understand for non-infrastructure stakeholders.