Applied AI Engineer II

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Weekday (YC W21)

WebsiteLinkedInAll Job Openings

11 - 50 employees

Founded 2021

☁️ SaaS

🎯 Recruiter

Human Resources • SaaS • Recruitment

Weekday is a modern recruitment platform that combines AI technologies with a vast database of potential candidates, aiming to streamline the hiring process for companies in India. They offer various services, including a proactive outreach approach that helps employers connect with top talent, as well as tools for candidates to easily apply for jobs. Weekday's emphasis on candidate engagement through multiple channels, including email, WhatsApp, and phone calls, sets it apart in the competitive landscape of recruitment agencies.

📋 Description

• Collaborate with experienced data scientists and software engineers to gain insights into building scalable and efficient data pipelines, model training, and deployment systems. • Troubleshoot issues in the entire machine learning infrastructure, from Linux, Docker, and Kubernetes up to the highest levels of our ML stack. Resolve issues, improve system performance, and make our stack the best in the industry. • Assist in the design and development of on-premises MLOps solutions to support the delivery of machine learning models, and a seamless handover between research and productionization of ML artifacts. • Drive and uphold high engineering standards, bringing consistency to codebases encountered and ensuring software is adequately reviewed, tested, and integrated. • Optimize existing models for better performance and throughput. • Incorporate ML model training, validation, and evaluation settings in addition to traditional coding tests like unit and integration testing. • Build and maintain tools for deployment, monitoring, and operations. • Continuously refine and enhance CI/CD workflows to support the evolving needs of the machine learning infrastructure.

🎯 Requirements

• 3+ years of experience in MLOps or full stack Machine Learning • Good programming skills in a modern programming language (Python, Scientific Python Stack, Cuda). • Understanding of the MLOps life cycle and experience with MLOps workflows. • Experience with tools & practices of the trade, such as Kubernetes, GCP/AWS/Azure, CI/CD, common ML frameworks, and data management. • A keen interest in machine learning engineering and a willingness to explore how it can be scaled effectively. • Strong desire to learn and good communication skills, with an enthusiasm for collaborative problem-solving.