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Technical Recruiter

Pubblicato il 06-10-2026 - Hired in Pordenone

- Role: Technical Recruiter (Remote)
- Location: Remote (Work from Anywhere)

Role Overview: This role focuses on developing and refining an AI-powered recruitment tool named Zara to automate candidate sourcing, screening, and engagement processes. The position requires deep technical expertise in machine learning and AI systems while collaborating with cross-functional teams to deploy scalable solutions.
Key Responsibilities:
- Design, implement, and optimize machine learning models to improve Zara's candidate matching accuracy and efficiency.
- Develop and maintain backend infrastructure supporting natural language processing for resume parsing and job description analysis.
- Integrate Zara with applicant tracking systems, HR databases, and third-party job platforms via APIs.
- Collaborate with data scientists and product teams to refine AI training datasets and evaluate model performance metrics.
- Ensure compliance with privacy regulations and ethical AI standards in recruitment workflows.

Required Skills & Qualifications:
- Proficiency in Python,



including experience with machine learning frameworks like TensorFlow or PyTorch.
- Strong background in natural language processing, including experience with libraries such as spaCy or Hugging Face.
- Experience building and deploying scalable AI systems in production environments.
- Familiarity with cloud platforms (AWS, GCP, or Azure) and containerization tools like Docker.
- Knowledge of SQL and NoSQL databases for data extraction and integration.
- Understanding of ethical AI principles and bias mitigation techniques in recruitment systems.
- Experience with RESTful APIs and microservices architecture.
- Background in data pipeline development and ETL processes.

More About the Opportunity: This role offers a unique opportunity to work on a cutting-edge AI recruiter system used by organizations worldwide. The position contributes to reducing hiring bias and improving talent acquisition efficiency through technol

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