Company Overview:

Quickhyre AI is a cutting-edge recruitment technology platform dedicated to transforming the hiring landscape through intelligent automation and data-driven insights. By leveraging advanced machine learning models, the company streamlines the talent acquisition process for enterprises, enabling recruiters to identify and engage top-tier candidates with unprecedented speed and precision. Operating at the intersection of HR-tech and artificial intelligence, Quickhyre AI is scaling rapidly to redefine how global organizations build their teams.

Role Overview:

As a Machine Learning Engineer at Quickhyre AI, you will be at the forefront of developing and deploying scalable AI models that power our core recruitment engine. You will work closely with product managers and data scientists to translate complex business requirements into high-performance algorithms that enhance candidate matching and predictive analytics. Your contributions will directly influence the efficiency of our platform, ensuring that our clients can make smarter, faster hiring decisions while maintaining high standards of data integrity and model accuracy.

Key Responsibilities:

- Design and implement robust machine learning pipelines to process large-scale recruitment datasets, ensuring high availability and performance for end-users.

- Develop and fine-tune predictive models for candidate ranking and skill matching to improve the quality of search results for our enterprise clients.

- Collaborate with cross-functional engineering teams to integrate AI-driven features into the production environment, directly impacting user engagement and platform conversion rates.

- Monitor model performance in production, conducting regular audits and retraining cycles to mitigate bias and maintain accuracy as data patterns evolve.

- Optimize existing algorithms for latency and scalability, ensuring the platform remains responsive as the volume of processed applications grows.

Required Skillset:

- Demonstrated proficiency in building and deploying machine learning models using Python and frameworks such as PyTorch, TensorFlow, or Scikit-learn.

- Strong understanding of natural language processing (NLP) techniques to analyze resumes, job descriptions, and professional profiles effectively.

- Proven ability to manage end-to-end model lifecycles, from data preprocessing and feature engineering to deployment and monitoring in cloud environments.

- Excellent communication skills with the ability to translate technical model outcomes into actionable insights for non-technical stakeholders.

- Self-driven mindset with the discipline to thrive in a fully remote environment, coordinating effectively with distributed teams across different time zones.

- A degree in Computer Science, Mathematics, or a related quantitative field, supported by 3 - 5 years of hands-on experience in developing production-grade AI solutions.