Senior AI Data Platform Engineer
Tasks
- Build MCP compatible agent data APIs and tool servers
- Design streaming first data pipelines to reduce latency
- Extend ML Attribute Store for low latency online serving
- Implement agentic framework for anomaly detection and self healing
- Improve platform observability and incident response automation
- Integrate AI powered developer tools to accelerate engineering velocity
- Partner with data science personalization and experimentation teams to reduce engineering toil
- Tune performance optimize cost and own on call operations
Perks/Benefits
- N/A
Skills/Tech-stack
A2A | Amazon Kinesis | Amazon Web Services | Apache Airflow | Apache Flink | Apache Spark | Auto rollback | Autogen | Azure | CI/CD | Context window | Context window management | Cost Optimization | CrewAI | Data discovery | Databricks | Databricks Vector Search | Datahub | Delta Lake | Delta Live Tables | Delta Live) | Docker | Document chunking | Faiss | Feature Store | Feature serving | Function Calling | Go | Incident Response | Intelligent retry | Java | Kafka | Knowledge graph | Kubernetes | LLM integration | Langchain | Langgraph | Machine Learning | Machine learning feature store | Metadata Driven | Metadata driven data discovery | Model Context Protocol | Observability | Offline feature computation | Online Feature Serving | OpenMetadata | Output parsing | Performance Tuning | Pinecone | Prompt engineering | Python | RAG | Retrieval evaluation | SQL | Scala | Semantic layer | Spark Structured Streaming | Structured Output | Structured Streaming | Structured output parsing | Training serving skew mitigation | Training-serving skew | Unity Catalog | Vector Database | Vector Search | Weaviate | Web Services | Window management
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
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