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BHEEMA SABILLA
Mid-level Data Engineer specializing in Lakehouse, Streaming, and ML/LLM data systems
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About
Built and productionized an enterprise retrieval-augmented generation platform for internal knowledge over large unstructured corpora, emphasizing trust via strict citation/grounding and hybrid retrieval (BM25 + FAISS + cross-encoder re-ranking). Demonstrates strong scaling and cost/latency optimization through incremental indexing/embedding and index partitioning, plus disciplined evaluation/observability practices. Has experience operationalizing pipelines with Airflow/Databricks/GitHub Actions and partnering closely with risk & compliance stakeholders on auditability requirements.
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Key Strengths
- Built and deployed an enterprise RAG platform with source-cited, trustworthy answers over unstructured data
- Strong retrieval architecture design (hybrid BM25 + FAISS with cross-encoder re-ranking)
- Scales retrieval systems via incremental indexing/embedding and index partitioning to control latency and cost
- Reliability-focused AI agent/workflow design with clear success metrics and observability
- Rigorous evaluation approach (offline test sets, regression tests, edge-case testing, production KPI monitoring)
- Evidence-driven model/retrieval/prompt selection based on business constraints (accuracy, latency, cost, explainability, compliance)
- Effective collaboration with risk/compliance stakeholders; translates needs into measurable, auditable technical requirements
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