Applied AI/ML Engineer
Tasks
- Build and evaluate models for classification ranking prediction NLP anomaly detection
- Contribute to model serving scalability and cost optimization decisions
- Design machine learning features for production systems
- Develop and deploy machine learning and AI models
- Develop and integrate Generative AI solutions
- Document approaches assumptions and tradeoffs
- Evaluate model performance bias drift reliability
- Implement data pipelines and feature engineering workflows
- Integrate models into APIs services and user applications
- Translate business and user needs into ML experiments and metrics
Perks/Benefits
Skills/Tech-stack
API Integration | Anomaly Detection | Apache Spark | CI/CD | Cloud Platforms | Code review | Data Pipelines | Deep learning | Embeddings | Experiment tracking | Feature Engineering | Language Models | Language Processing | Large Language Models | MLOps | Machine Learning | Model Evaluation | Model Monitoring | Natural Language | Natural Language Processing | Pandas | Prompt engineering | PyTorch | Python | Retrieval-Augmented Generation | SQL | Scikit-learn | Supervised Learning | TensorFlow | Testing | Unsupervised Learning | Version control
Education
N/A
Regions
Countries
States
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