Senior Applied AI Engineer
Tasks
- Build data processing and training inference and evaluation pipelines
- Design and maintain scalable AI APIs and backend services
- Develop evaluation methodologies benchmark datasets and quality assurance mechanisms
- Develop production AI and machine learning systems
- Implement MLOps best practices with CI CD model monitoring and governance
- Improve model performance across accuracy latency reliability scalability and cost
- Integrate AI capabilities into enterprise workflows
- Monitor production environments troubleshoot issues and enhance model and infrastructure performance
- Research and evaluate emerging AI techniques tools and frameworks
- Train fine tune optimize and maintain machine learning models
Perks/Benefits
- Autonomy and ownership
- Continuous learning
- Fully remote
- High-growth environment
- Professional development
- Structured recruitment process
- Work-life balance
Skills/Tech-stack
API Design | AWS | Amazon SageMaker | Automated retraining | CI/CD | Cloud platform | Data Pipelines | Deep learning | Distributed Systems | Docker | Embeddings | Evaluation benchmarks | Google Cloud | Google Cloud Platform | Kubernetes | Language Models | Large Language Models | MLOps | Machine Learning | Microsoft Azure | Model Monitoring | Observability | PostgreSQL | PyTorch | Python | Retrieval-Augmented Generation | Scikit-learn | TensorFlow | Vector Databases | Vertex AI
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