Machine Learning Engineer
Turkey, across global time zonesFull-Time
Salary not disclosed
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Job Details
- Required Skills
- AWSPythonMachine LearningProblem SolvingDeep LearningLLMComputer Vision
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.
Responsibilities
- 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.
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