👋 At ContactCars, we drive innovation. We build smart systems that transform how people interact with automotive services.

ContactCars is seeking a highly motivated AI/ML Engineer with 2 years of hands-on experience in machine learning, data science, and large language models (LLMs). The ideal candidate will be passionate about building scalable AI solutions and deploying data-driven systems

in real-world environments.

🕓 Start Date: Immediate 

 🧑‍💻 Contract Type: Full-time / hybrid

🛠️ How the Hiring Quest Works

• Register for the quest

• After the registration period ends, you’ll receive the full task details

• Submit your solution before the deadline

• Top candidates will be invited to a review session

• One candidate will be hired, others may be shortlisted for future opportunities

🔍 Who We’re Looking For

 ✅ 2 years of experience in AI/ML engineering or data science.

 ✅ Solid understanding of data science fundamentals, including:

o Statistical analysis

o Hypothesis testing

o Data cleaning and preprocessing

✅ Proficient in Python and experienced with SQL for data manipulation.

✅ Hands-on experience with deep learning frameworks such as TensorFlow or

PyTorch.

✅ Strong working knowledge of:

o Scikit-learn, Pandas and NumPy

o Data visualization tools like matplotlib, seaborn, and plotly

✅ Experienced in Exploratory Data Analysis (EDA) and feature engineering.

✅ Familiar with model deployment techniques (e.g., REST APIs, batch pipelines,

Docker).

✅ Able to scrape and structure semi-structured web data efficiently.

✅ Understanding of Natural Language Processing (NLP) methods and LLMs (e.g., GPT,

BERT, RAG).

✅  Experience working with LLMs, including:

o Fine-tuning with LoRA and QLoRA

o Designing and building Agentic AI systems

🎯 Your Mission: 

Task 1 : Fine-Tune a Large Language Model (LLM)

You have access to a pre-trained LLM and a dataset of more than 1,000

real estate listings with their final sale prices. Design a fine-tuning approach to create a

model that can predict property prices for new listings. Your solution must implement Low-

Rank Adaptation (LoRA) techniques and apply appropriate quantization methods to

optimize model performance and deployment efficiency. 

Task 2: Agent AI Design Task

Design a simple AI agent that helps users find and reserve meeting

rooms in our office. The agent should understand availability, room features, and handle

scheduling conflicts. Your solution must leverage either LangGraph or AutoGen

frameworks for agent implementation. Create a flow diagram showing the key components,

decision points, and external systems it would interact with.

Requirements with all details here

📊 Evaluation Criteria

LLM Fine-Tuning Design 25%

How well the candidate designs and implements the fine-tuning strategy (LoRA, quantization, data pipeline).

AI Agent System Architecture 20%

How well the agent system is architected: clarity of states, flow, external integrations, and decision logic.

Code Quality and Organization 25%

Clean, modular, maintainable code (separation of concerns, readable structure, reusable components).

Business Understanding 15%

How well the candidate understands the business goal of each task, as shown in their explanations and suggested improvements.

Documentation and Communication 15%

Clarity, completeness, and professionalism of submitted documents, diagrams, and README instructions.

📩 After Submission

• Top candidates will be invited to a technical review session

• Final hiring decision will be made within 3–5 business days after the review session.

👉 Hiring decision within 3–5 business days after your review session.