Applied AI/ML Engineer
Tasks
- Apply supervised and unsupervised learning
- Build and evaluate classification ranking prediction NLP anomaly detection models
- Build embedding-based retrieval systems
- Contribute to model serving architecture decisions
- Create feature engineering pipelines
- Design machine learning models for production
- Develop and deploy AI-powered features
- Develop data pipelines for training and inference
- Develop generative AI solutions
- Document model approaches assumptions and tradeoffs
- Evaluate model performance bias drift reliability
- Implement retrieval-augmented generation workflows
- Integrate models into APIs and services
Perks/Benefits
Skills/Tech-stack
API Integration | Anomaly Detection | Apache Spark | Bias Mitigation | CI/CD | Classification | Code review | Data Pipelines | Deep learning | Embeddings | Experiment tracking | Feature Engineering | Language Models | Language Processing | Large Language Models | Machine Learning | Model Evaluation | Model Monitoring | Model Serving | Model Training | Natural Language | Natural Language Processing | Pandas | Prediction | PyTorch | Python | Ranking | Retrieval-Augmented Generation | SQL | Scikit-learn | Supervised Learning | TensorFlow | Testing | Unsupervised Learning | Version control
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
Regions
Countries
States
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