MLOps Engineer for AI Model Ecosystem

One of our leading AI platforms, specializing in Computer Vision and Generative AI, is hiring a MLOps Engineer for a fully remote role.

Key Responsibilities

• Identify trending open-source AI models with strong community adoption, import them into the Community, and validate them across real-world use cases.
• Create clear, engaging previews and demos—both technical and non-technical—that showcase model capabilities.
• Collaborate with Marketing to promote new models and generate compelling content around them.
• Engage with the open-source AI community to build relationships with original model authors and increase backlink visibility.
• Develop lightweight Python-based demos and utilities to highlight model performance and usability.

Impact

As an ML Community Ops Engineer, you will directly contribute to growing company's model ecosystem by adding cutting-edge AI models and making them accessible to users. Your work will expand the company's reach, improve discoverability, and ensure our platform remains at the forefront of open-source AI.

Requirements

• Strong experience developing, fine-tuning, and evaluating machine learning models, including familiarity with model architectures and key evaluation metrics.
• Expertise in deep learning frameworks (e.g., PyTorch, TensorFlow, JAX) and architectures such as transformers and CNNs.
• Actively follows AI and ML trends—staying current with emerging models, benchmarks, and communities.
• Proficiency in Python, with ability to write clean, efficient code for ML workflows and data pipelines.
• Experience working with cloud platforms (e.g., AWS, GCP, Azure) for model deployment and compute orchestration.
• Solid software engineering fundamentals, including Git, modular design, and code testing.
• Practical experience with data preprocessing, feature engineering, and analysis of large datasets.

Great to Have

• Strong experience developing, fine-tuning, and evaluating machine learning models, including familiarity with model architectures and key evaluation metrics.
• Expertise in deep learning frameworks (e.g., PyTorch, TensorFlow, JAX) and architectures such as transformers and CNNs.
• Actively follows AI and ML trends—staying current with emerging models, benchmarks, and communities.
• Proficiency in Python, with ability to write clean, efficient code for ML workflows and data pipelines.
• Solid software engineering fundamentals, including Git, modular design, and code testing.
• Practical experience with data preprocessing, feature engineering, and analysis of large datasets.