Machine Learning Engineer (AI)

TL;DR

Machine Learning Engineer (AI/Python): Designing and developing scalable, fault-tolerant AI/ML services and pipelines in a cloud-based environment with an accent on LLMs, foundational models, and computer vision. Focus on architecting high-performance pipelines, fine-tuning models, and integrating emerging frameworks for mission-critical production systems.

Location: Remote (Global) — Work from anywhere you like

Company

One of the world’s largest virtual companies providing IT services with a focus on flexibility and global growth.

What you will do

• Design, develop, and architect high-performance AI/ML services and pipelines within a cloud-based (AWS) environment.
• Contribute to R&D initiatives, focusing on service integration with emerging frameworks and platforms.
• Manage the technical resolution of issues on mission-critical production systems following the "you build it, you own it, you run it" philosophy.
• Engineer scalable, fault-tolerant, and elegant solutions to solve complex AI challenges.

Requirements

• 3+ years of professional experience with Python.
• 4+ years of experience in machine learning, deep learning, large language models (LLMs), and foundational models.
• Hands-on expertise with AWS cloud architectures specifically for AI/ML pipelines.
• Experience building automated workflows for data preparation, curation, and annotation.
• Proven track record in fine-tuning, training, validating, and deploying models in production settings.
• Experience in vision-based AI, including object detection, image segmentation, and automated data labeling.

Nice to have

• Experience with NVIDIA Enterprise AI Solutions, AWS SageMaker, AWS Bedrock, AWS Clean Rooms, and Data Lakes.
• Experience building classification models for image, text, audio, and video (e.g., shot/scene detection, attention scoring).
• Familiarity with Media and Entertainment industry file formats (IMF, Pro Tools, MXF, 3D).

Culture & Benefits

• Full flexibility: pick your own time zone and preferred working hours.
• True remote freedom: work from anywhere in the world with a stable internet connection.
• Investment in growth through paid training and professional opportunities.
• Consistent workload with a flow of engaging and challenging tasks.
• Performance-based recognition including annual pay increases.