Lead Applied AI Software Engineer ( AI)
Tasks
- Architect end-to-end AI systems
- Build RAG pipelines
- Build data preprocessing pipelines
- Collaborate with platform teams for GPU readiness
- Define prompt engineering standards
- Deploy AI systems to production
- Design data ingestion architectures
- Ensure healthcare compliance and ethical AI
- Implement evaluation frameworks
- Implement observability logging and tracing
- Implement vector databases and hybrid search
- Integrate multi model systems
- Mentor engineers and lead code reviews
- Optimize model performance
- Orchestrate AI agents
- Run A B testing and iterate prompts and retrieval
- Serve model serving infrastructure
- Set reliability patterns circuit breakers graceful degradation
Perks/Benefits
- N/A
Skills/Tech-stack
A/B | A/B Testing | Agent Orchestration | B testing | Caching | Change Management | Circuit Breakers | Data Governance | Data Preprocessing | Data Quality | Data quality monitoring | Embedding Models | FastAPI | GPU | Hybrid search | LLM APIs | Language Models | Large Language Models | Logging | Model Evaluation | Model Risk Management | Model risk | Monitoring | Observability | Prompt engineering | Python | Quality monitoring | RAG | React | Risk Management | Tracing | Vector Databases
Education
Roles
AI Engineer | Applied AI Engineer | Engineer | Lead | Lead Applied AI Engineer
Regions
Countries
States
Cities
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