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Kalyani Chittipolu
Mid-level Data Engineer specializing in AI/ML platforms and cloud data pipelines
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About
Built and shipped an LLM-powered data quality assistant that generates maintainable validation checks from metadata while executing validations via Great Expectations, exposed through FastAPI and integrated into Airflow-managed pipelines. Emphasizes production reliability (structured outputs, guardrails, monitoring, versioning, human review) and works closely with compliance/operations teams to deliver clear, auditable, user-friendly AI outputs.
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Key Strengths
- Built and deployed an LLM-powered data quality assistant integrated into production data pipelines
- Strong LLM reliability engineering: strict structured outputs, validation, guardrails, and safe fallbacks
- Privacy-aware design: limited LLM inputs to metadata to avoid sensitive data exposure
- Operational excellence with Airflow orchestration (modular DAGs, retries, alerting) across ingestion, transforms, DQ, and publishing
- Pragmatic model/RAG selection and iteration using real test cases with quality/cost/latency tradeoffs
- Designs AI agents as modular, testable workflows with measurable evaluation (correctness, consistency, latency) and production monitoring
- Effective collaboration with non-technical compliance/operations stakeholders; delivered explainable, auditable outputs that fit day-to-day workflows
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