AI Solutions Engineer
Tasks
- Build AI assistants and agents end to end
- Build indexing and access controls
- Collaborate with Data Engineering on data quality
- Deploy models using MLOps pipelines
- Design and maintain retrieval pipelines
- Document failure modes and edge cases
- Engineer prompts and iterate
- Implement chunking strategy and metadata schema
- Implement cost controls and runbooks
- Integrate workflow automations
- Monitor alert and manage rollbacks
- Optimize queries for retrieval
- Own code quality and handoff
- Turn signed off specifications into production AI
- Write and test and evaluate LLM applications
- Write tests and maintain documentation
Perks/Benefits
Skills/Tech-stack
Access Control | Agent Frameworks | Alerting | Anthropic API | Azure OpenAI | Chunking | Cost Optimization | Document processing | Evaluation | Google Vertex | Google Vertex AI | Indexing | Langchain | Language Models | Large Language Models | Llamaindex | MLOps | Metadata Schema | Monitoring | Multi Step Flows | Operational runbooks | Orchestration | Precision-recall | Prompt engineering | Python | Query Optimization | RAG | Retrieval-Augmented Generation | Rollback | Security | Software Documentation | Structured prompting | Tool use | Unit Testing | Vector Databases | Vertex AI
Education
N/A
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