# Stop Building Toy Chatbots: The 5 AI Engineering Projects That Will Get You Hired

> Source: <https://pub.towardsai.net/stop-building-toy-chatbots-the-5-ai-engineering-projects-that-will-get-you-hired-d569eebe89fd?source=rss----98111c9905da---4>
> Published: 2026-07-26 12:01:01+00:00

If you put a basic “PDF Chatbot with LangChain and Streamlit” on your resume today, most engineering managers will swipe left in under five seconds.

Why? Because calling an API with three lines of boilerplate code isn’t AI engineering — it’s basic scripting.

In 2026, tech companies aren’t looking for developers who can connect an LLM to a vector store. They want engineers who understand **system latency, evaluation loops, stateful multi-agent workflows, and cost guardrails.**

If you want to stand out in a sea of generic GenAI portfolios, here are the 5 production-grade projects you need to build — and exactly what to include in each.

Standard RAG (Retrieval-Augmented Generation) breaks down the moment a query is ambiguous or the vector search returns low-quality chunks. An **agentic RAG system** doesn’t just blindly pass retrieved context to the LLM—it dynamically evaluates and refines its own search strategy.

```
# Conceptual loop using LangGraph or custom state machinedef grade_retrieved_docs(state):    docs = state["documents"]    question = state["question"]        relevant_docs = []    for doc in docs:        if document_evaluator.is_relevant(doc, question):            relevant_docs.append(doc)                # Fallback if no relevant docs found: trigger query re-write or web search    if not relevant_docs:        return "rewrite_query"    return "generate_answer"
```

Why it gets you hired:It proves you understand that vector retrieval isn’t 100% accurate and that you know how to build fault-tolerant retrieval pipelines.

Single-prompt completion models fail when tasks require multiple steps, memory, and specialized tool executions. A multi-agent framework splits complex goals into dedicated roles (e.g., Researcher, Coder, Evaluator) that communicate via shared state.

Build an **Automated Code Review & Security Auditor Agent** that:

Key Feature to Implement:Add aHuman-in-the-Loopcheckpoint where a user must approve high-severity security actions before any auto-remediation PR is created.

Most AI projects fail to reach production because teams have no systematic way to measure whether a prompt change broke expected outputs.

Building an **Eval Pipeline** proves you think like a software engineer, not just an experimental prompt crafter.

``` python
# Simple assertion check pattern for CI/CD pipelinesfrom ragas import evaluatefrom ragas.metrics import faithfulness, answer_relevancedef run_evaluation_suite(test_dataset):    results = evaluate(        dataset=test_dataset,        metrics=[faithfulness, answer_relevance]    )        # Block PR deployment if quality drops below threshold    assert results["faithfulness"] >= 0.85, "Faithfulness score below threshold!"    assert results["answer_relevance"] >= 0.90, "Relevance score below threshold!"
```

Sending every simple task to a cloud-hosted frontier model is expensive and slow. Companies want to deploy smaller, highly specialized Small Language Models (SLMs like Phi-3, Llama-3–8B, or Qwen2) on self-hosted infrastructure.

Why it gets you hired:Demonstrates real MLOps, model quantization, containerization, and cost optimization skills that directly impact a company’s bottom line.

Text chatbots are oversaturated. Real-time streaming voice/vision interactions represent the cutting edge of AI product engineering.

Build a **Real-Time Interactive AI Technical Interviewer** using streaming protocols:

```
# Handling streaming audio chunks over WebSocket@app.websocket("/ws/audio")async def handle_audio_stream(websocket: WebSocket):    await websocket.accept()    async for chunk in websocket.iter_bytes():        # Stream raw audio bytes directly to STT engine        transcript = await stt_service.stream_transcribe(chunk)        if transcript.is_final:            # Stream LLM tokens to TTS immediately            async for audio_chunk in tts_service.stream_response(transcript.text):                await websocket.send_bytes(audio_chunk)
```

No matter which 3 of these 5 projects you choose to build, follow this exact structure in your GitHub repositories:

[Stop Building Toy Chatbots: The 5 AI Engineering Projects That Will Get You Hired](https://pub.towardsai.net/stop-building-toy-chatbots-the-5-ai-engineering-projects-that-will-get-you-hired-d569eebe89fd) was originally published in [Towards AI](https://pub.towardsai.net) on Medium, where people are continuing the conversation by highlighting and responding to this story.