Machine Learning Engineer

Overview

Accountabilities

• Design, develop, and maintain scalable AI/ML services and pipelines in cloud-based environments.
• Build and optimize machine learning workflows, including data preparation, curation, annotation, training, validation, and deployment.
• Contribute to research and development initiatives involving emerging AI frameworks, models, and technologies.
• Deploy and support production-grade machine learning systems with a strong focus on reliability, scalability, and performance.
• Troubleshoot and resolve issues in mission-critical production environments following an end-to-end ownership mindset (“you build it, you run it”).
• Develop vision-based AI solutions, including object detection, image segmentation, labeling, and classification tasks.
• Collaborate with cross-functional and distributed teams to deliver high-quality, production-ready AI solutions.
• Continuously improve system architecture, model performance, and operational efficiency through experimentation and optimization.

Requirements

• 3+ years of hands-on experience in Python development.
• 4+ years of experience in machine learning, deep learning, or large language model-based systems.
• Strong experience working with cloud platforms (preferably AWS) for building and deploying AI/ML pipelines.
• Experience designing scalable workflows for data ingestion, processing, labeling, and secure storage for model training.
• Proven experience in training, fine-tuning, testing, and deploying machine learning models in production environments.
• Solid understanding of computer vision techniques such as object detection, segmentation, and image classification.
• Strong problem-solving skills with the ability to design scalable, fault-tolerant, and efficient systems.
• Experience working independently and collaboratively in distributed, remote-first teams.
• Excellent communication skills for effective collaboration across global engineering teams.
• Familiarity with tools such as AWS SageMaker, AWS Bedrock, or NVIDIA AI platforms is a plus.
• Exposure to media, audio, video, or image processing workflows is considered an advantage.

Benefits

• Competitive compensation based on skills and experience, with performance-based adjustments.
• Fully remote work with flexible scheduling across global time zones.
• Opportunity to work with cutting-edge AI, ML, and cloud technologies.
• Continuous learning and professional development support, including paid training opportunities.
• Exposure to challenging, high-impact projects in a global engineering environment.
• Long-term engagement with a steady flow of meaningful and technically challenging work.
• Recognition and rewards based on performance and contribution.
• Freedom to work from anywhere with a stable internet connection.
• Inclusive and diverse international team culture.

How Jobgether Works

Data Privacy Notice