{"n":"AI Product Engineers","p":40,"c":12,"o":10,"m":125600,"h":60,"a":92,"rg":[["EMEA",45],["Americas",33],["APAC",22]],"b":[["Junior",0,67000,78000,90000],["Mid-level",67,91000,106000,123000],["Senior",33,111000,129000,150000],["Lead",0,126000,146000,169000],["Principal",0,153000,178000,206000]],"rl":[["AI Data Trainers","ai-data-trainers",40],["Ai Engineer & Researchers","ai-engineer-researchers",40],["Cloud Engineering Managers","cloud-engineering-managers",40],["Community Engineers","community-engineers",40],["Developer Success Engineers","developer-success-engineers",40],["Enterprise Presales Engineers","enterprise-presales-engineers",40]],"r":[["Heroine","Senior AI Engineer","EMEA",96,58,120000],["The Founder","Senior Blockchain Engineer","Americas",95,72,150000],["The Tracker","Blockchain Engineer","APAC",95,72,150000],["The Hero","Applied AI Engineer","Americas",94,58,120000],["Captain Marvel","Senior Blockchain Engineer","EMEA",91,65,135000],["Waterbender","Blockchain Engineer","EMEA",90,58,120000],["Thor","Blockchain Solutions Engineer","APAC",89,70,145000],["Hero","Software Engineer - AI Focused","Americas",87,48,100000],["The Bear","AI Safety Engineer","EMEA",87,46,95000]],"j":[["AI Model Development",["Designing and implementing deep learning architectures.","Selecting appropriate frameworks for the AI solution.","Tuning hyperparameters for optimal model performance.","Developing training pipelines for large datasets."]],["Data Management and Processing",["Cleaning and preprocessing raw datasets for training.","Creating data augmentation techniques for robustness.","Implementing data pipelines using Apache Airflow.","Monitoring data quality and consistency throughout lifecycle."]],["Model Evaluation and Testing",["Developing metrics to evaluate model accuracy and performance.","Conducting A/B testing to compare model variants.","Creating visualizations for model performance insights.","Performing cross-validation to avoid overfitting."]],["Deployment and Maintenance",["Deploying models on cloud platforms like AWS or GCP.","Implementing continuous integration/continuous deployment (CI/CD) practices.","Monitoring model inference for performance degradation.","Updating models based on incoming new data regularly."]]]}