Quantitative Developer
Tasks
- Add quality gates
- Build model evaluation harnesses
- Build peer selection systems
- Build production ML models
- Build ranking systems
- Build recommendation systems
- Build retrieval-augmented generation workflows
- Build semantic matching systems
- Create AI assisted review workflows
- Create benchmark datasets
- Create evaluation frameworks for AI agents
- Create inference workflows
- Deploy models
- Design agentic AI workflows
- Detect data drift
- Develop feature pipelines
- Develop model serving APIs
- Develop unstructured data classification
- Establish MLOps experiment tracking
- Implement human in the loop review
- Implement model monitoring
- Implement model versioning
- Integrate embeddings
- Orchestrate agent workflows
- Run backtests
- Run batch jobs
- Support rollback and audit trails
- Translate investment workflow needs into ML solutions
Perks/Benefits
- N/A
Skills/Tech-stack
APIs | Agentic AI | Batch Processing | CI/CD | Containerization | Deployment | Docker | Drift Detection | Embeddings | Experiment tracking | Feature Engineering | Inference | Information Extraction | LLM | MLOps | Machine Learning | Model Evaluation | Model Registry | Model Training | Model versioning | Monitoring | Observability | Planning | Python | Queues | Ranking | Recommendation Systems | Retrieval-Augmented Generation | Rollback | SQL | Semantic Search | Tool use | Vector Databases
Education
Regions
Countries
States
Cities
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