← Zurück zu den ErgebnissenAI Engineer
IO Tech Solutions Limited
Remote-Arbeit
- Standort
- Remote (China)
- Arbeitszeit
- Full Time
- Veröffentlicht
- 11. Okt. 2026
Prüfe vor der Bewerbung auf der Website des Arbeitgebers, ob die Stelle noch offen ist und welche Bedingungen gelten.
Stellenbeschreibung
Die Oberfläche ist auf Deutsch. Die Titel und Beschreibungen der Arbeitgeber bleiben in ihrer ursprünglichen Veröffentlichungssprache, die Englisch sein kann.
Overview:
We are seeking a highly skilled AI Engineer to design, develop, and deploy machine learning and artificial intelligence solutions. The ideal candidate has a strong background in computer science, mathematics, and data science, with hands-on experience building and deploying AI models that solve real-world problems.
Key Responsibilities:
Design, develop, and optimize machine learning and AI models for production use.
Collaborate with data scientists, engineers, and product teams to integrate AI solutions into applications.
Preprocess, clean, and analyze structured and unstructured datasets.
Develop and maintain scalable pipelines for training, evaluation, and deployment of ML models.
Monitor, test, and improve the performance of AI models in production.
Stay updated with the latest AI/ML research, frameworks, and best practices.
Ensure AI systems meet ethical, responsible AI, and security standards.
Qualifications:
Bachelors or Masters degree in Computer Science, Data Science, Artificial Intelligence, or related field (PhD is a plus).
Proven experience in machine learning, deep learning, or natural language processing.
Strong proficiency in Python and ML frameworks (TensorFlow, PyTorch, scikit-learn, Hugging Face, etc.).
Solid understanding of algorithms, data structures, probability, and statistics.
Experience with data handling, feature engineering, and model evaluation.
Familiarity with cloud platforms (AWS, GCP, Azure) and ML Ops tools.
Knowledge of APIs, microservices, and deployment practices.
Nice-to-Have Skills:
Experience with large language models (LLMs), generative AI, or reinforcement learning.
Familiarity with vector databases and semantic search.
Understanding of distributed computing (Spark, Ray, Dask).
Knowledge of CI/CD pipelines for ML (MLflow, Kubeflow, Vertex AI).
Background in computer vision, NLP, or recommendation systems.
Originally posted on Himalayas