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

CTC offered: Upto 25 LPA

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.
Outstanding 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.

📌 Ml Engineer (Remote) (India)
🏢 Talentiser
📍 India