Senior AI/ML engineer

We are seeking a highly skilled AI/ML Engineer to lead the digital transformation of our software development and testing workflows.

Our client is a leading American multinational information technology corporation, headquartered in California, USA, recognized as one of the world's largest companies in the IT sector. The company specializes in developing and manufacturing computing, data storage, and networking hardware, alongside designing software and delivering services. Their major product lines include personal computing devices, enterprise servers, and a diverse range of printers and imaging products.

The project involves the development of a comprehensive software package for a large OEM vendor. This software is designed for both enterprise and consumer environments and is installed on endpoint devices. It integrates various cutting-edge services, such as AI companion tools, workplace management solutions, and Smart Office integration.

As part of the GlobalLogic team, you will grow, be challenged, and expand your skill set working alongside highly experienced and talented people.

If this sounds like an exciting opportunity for you, send over your CV!



Job Description

• 4+ years of experience as an AI/ML Engineer or in a similar data-intensive role
• Hands-on expertise in AI/ML pipelines and model lifecycle management
• Strong experience in data curation, feature engineering, and preparing datasets for ML
• Proven track record integrating ML solutions into CI/CD pipelines
• Experience training, fine-tuning, evaluating, and deploying ML models in production environments
• Proficiency in Python for ML integration and automation
• Knowledge of Jira or other issue tracking systems
• Upper-intermediate English proficiency (spoken and written)
   

Job Responsibilities

• Lead AI-driven initiatives to integrate smart automation into CI/CD pipelines, accelerating development and reducing manual overhead
• Curate, annotate, and optimize datasets to train models that improve engineering processes
• Build recommendation systems and fine-tune models to enhance team decision-making and workflow efficiency
• Implement AI-based Jira issue triaging to streamline task management
• Collaborate closely with developers and product teams to embed ML solutions that deliver measurable impact