Staff Engineer - AI
Tasks
- Automate internal workflows using ML and LLMs
- Balance accuracy latency explainability reliability and cost
- Build AI tools and platforms for small teams
- Build model serving and inference pipelines
- Build production grade AI systems in operational workflows
- Define AI platform architecture across teams
- Design AI systems for regulated environments
- Design and ship AI ML systems in payment workflows
- Design decision systems using rules ML inference and self healing
- Ensure security privacy auditability and explainability
- Evaluate and adopt AI data and automation technologies
- Implement feature engineering and online offline consistency
- Implement safe deployment practices
- Mitigate bias failure modes and unintended behavior
- Monitor data quality model performance and system health
- Operate and evolve ML infrastructure
- Own end to end lifecycle of AI systems
- Own model behavior in production
- Provide technical leadership and direction across initiatives
- Replace manual reviews with intelligent systems
- Use event driven streaming pipelines and real time processing
Perks/Benefits
- N/A
Skills/Tech-stack
AWS | Artificial Intelligence | Azure | Backtesting | C# | CI/CD | Canary Releases | Data Quality | Data quality monitoring | Distributed Systems | Docker | Event Driven | Event-driven architecture | Explainable AI | Feature Engineering | Java | Kubernetes | LLM | MLOps | Machine Learning | Microservices | Model Monitoring | Model Serving | Python | Quality monitoring | Real Time | Real-time Processing | Rollback | Shadow testing | Streaming Data | Time processing
Education
N/A
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