Development Engineer: Applied AI/ML and Intelligent systems
Tasks
- Build statistical/machine learning models
- Deploy edge AI for smart equipment
- Develop AI agents workflows
- Develop applied AI ML solutions
- Develop generative AI applications
- Document and communicate results
- Fuse multimodal data
- Implement MLOps pipelines
- Perform industrial time series analytics
- Validate models in experimental environments
Perks/Benefits
Skills/Tech-stack
Agentic Workflows | Anomaly Detection | Artificial Intelligence | CI/CD | Classification | Containerization | Data Generation | Data Pipelines | Data Preparation | Data Processing | Embedded deployment | Experiment tracking | Feature Engineering | Generative AI | LLM | Lightweight Inference | MLOps | Machine Learning | Model Compression | Model Development | Model Monitoring | Model versioning | Multimodal Learning | Orchestration | Prediction | Python | Real Time | Real-time Inference | Regression | Retrieval-Augmented Generation | Scientific Computing | Statistical Machine Learning | Synthetic Data Generation | Synthetic data | Time Series | Uncertainty Estimation
Education
Roles
AI | AI Engineer | Development Engineer | Engineer | Learning Engineer | Machine Learning Engineer
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