About the role
Bridge raw data and functional AI, training machine learning models, creating data pipelines, and integrating AI capabilities into existing production environments.
Key Responsibilities
Model Development: Select algorithms, train models, tune hyperparameters, and experiment with architectures (ranging from classical ML to Generative AI and deep learning.
System Infrastructure: Build and manage scalable data pipelines, automated training infrastructures, and production-ready APIs.
Deployment & Monitoring: Deploy AI tools, troubleshoot algorithm issues, and continuously monitor live models for performance drops or data drift.
Cross-Functional Collaboration: Partner with data scientists, software developers, and business stakeholders to translate project requirements into workable AI solutions.
Essential QualificationsEducation: Bachelor’s or Master’s degree in Computer Science, Data Science, Mathematics, or a related field.
Programming Languages: Proficiency in Python, R, Java, or C++.Frameworks & Tools: Experience with ML and Deep Learning frameworks like PyTorch, TensorFlow, Keras, or Spark.