Tasks:
- * Build model-serving services and APIs
- * Collaborate with algorithm, product, and QA teams
- * Deploy and productionize AI and agent models
- * Design and develop agent data pipelines
- * Develop, test, deploy, and maintain AI engineering modules
- * Implement multi-model orchestration and context management
- * Improve system reliability availability and scalability
- * Integrate agent models into business systems
- * Integrate data interfaces and manage concurrent requests
- * Monitor and maintain production models
- * Optimize agent workflow performance and development efficiency
- * Optimize model inference performance
- * Translate business requirements into engineering solutions
- * Write and maintain API, deployment, and test documentation
Perks/Benefits:
Skills/Tech stack required:
[Agent Frameworks] [Agent systems] [AI model] [AI Model Deployment] [API Development] [CI/CD] [Concurrent processing] [Containerization] [Context Management] [Data cleaning] [Data Pipelines] [Data Structures] [Data Structures and Algorithms] [Deep learning] [Docker] [Feature Engineering] [Git] [Go] [Inference Optimization] [Java] [Kubernetes] [Machine Learning] [Model Deployment] [Model Orchestration] [Model Serving] [Multi-model] [Multi-model orchestration] [Production Monitoring] [Python] [PyTorch] [TensorFlow]
Educational requirements:
[Bachelor's Degree]
Role(s):
[AI Application Engineer] [AI Software Engineer] [Application Engineer] [Engineer] [Learning Engineer] [Machine Learning Engineer] [Software Engineer]