Senior Data Engineer, Amazon Global Selling -AIT
Tasks
- Add data observability tooling
- Apply prompt engineering and context injection
- Assess hallucination risk and output quality
- Build LLM evaluation pipelines
- Build RAG pipelines
- Build unified AI ready data models
- Collect transform and store data
- Conduct design reviews
- Construct automated knowledge bases
- Curate fine tuning signals
- Define AI data architecture best practices
- Design and implement batch streaming data pipelines
- Design develop and deploy production AI agent systems
- Enable real time data access for agents
- Establish data governance and standardization policies
- Evaluate prompts systematically
- Implement data agent MCP server client integration
- Implement data quality, monitoring, and alerting
- Implement tool use frameworks
- Implement vector search and re ranking
- Integrate agent APIs
- Integrate multi source data into governed data layer
- Manage agent memory
- Mentor junior engineers
- Optimize document ingestion chunking and embeddings
- Orchestrate multi-agent workflows
- Provide technical guidance for AI agent and data stack
- Translate business requirements into technical solutions
Perks/Benefits
- N/A
Skills/Tech-stack
AI Agents | API Integration | Agent Orchestration | Amazon EMR | Apache Spark | Artificial Intelligence | Batch Processing | Data Modeling | Data Quality | Data Warehousing | Data pipeline | ELT | ETL | Embeddings | Fine Tuning | Hadoop | Hive | Java | Knowledge Base | Knowledge base construction | LLM Evaluation | Language Models | Large Language Models | Memory Management | Multi-Agent | Multi-agent orchestration | Node.js | Observability | Prompt engineering | Python | RAG Pipelines | Re-ranking | Retrieval-Augmented Generation | SQL | Scala | Streaming processing | Tool use | Vector Search
Education
N/A
Roles
AI | AI Engineer | Data Engineer | Engineer | Learning Engineer | Machine Learning Engineer | Senior Data Engineer
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