Machine Learning Engineer V
Tasks
- Build RAG systems and agent workflows
- Create reusable SDKs APIs workflows and developer tooling
- Design production grade AI and ML system architecture
- Develop prompt systems and evaluation pipelines
- Establish AI evaluation governance and production readiness standards
- Implement document ingestion extraction and validation services
- Implement model selection routing and spend optimization
- Improve classification extraction retrieval and decisioning systems
- Lead development of production AI systems
- Mentor engineers and lead design reviews
- Optimize ML and LLM services for reliability latency observability security cost
Perks/Benefits
- Bonuses
- Disability insurance
- Life insurance
- Paid parental leave
- Paid time off
- Private medical insurance
Skills/Tech-stack
APIs | Agentic Workflows | Asynchronous Workflows | Classification | Cloud Native | Distributed Systems | Document AI | Document Intelligence | Embeddings | Evaluation Pipelines | Generative AI | Information Extraction | Information Retrieval | LLM | LLM safety | Language Processing | Machine Learning | Model Governance | Model Serving | Model routing | Model selection | Monitoring | Natural Language | Natural Language Processing | OCR | OCR extraction | Observability | Prompt engineering | Python | RAG | Security | System design | Transformer Models
Education
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