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Dheeraj Vajjarapu
Mid-level AI/ML Engineer specializing in MLOps, NLP/LLMs, and computer vision
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About
Built and shipped a production LLM/RAG risk-case summarization and triage system used by fraud/compliance analysts, with strong grounding controls (evidence-cited outputs and refusal on low confidence). Demonstrates end-to-end ownership across retrieval quality, Airflow-orchestrated indexing pipelines, and compliance-grade privacy (PII redaction, RBAC, encrypted redacted logging, and auditable prompt/model versioning) plus a tight feedback loop with non-technical domain experts.
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Key Strengths
- Built and deployed production RAG system for fraud/compliance case summarization and triage
- Strong hallucination-control approach: evidence-tied claims with low-confidence refusal behavior
- Improves retrieval quality via hybrid search, metadata filtering, and reranking
- Privacy/compliance-by-design: PII redaction, RBAC/least privilege, encrypted redacted logs, audit-ready prompt/model versioning
- Production reliability mindset: Airflow orchestration with dependencies, retries, backfills, idempotent writes, and safe rollback (keep prior index live)
- Rigorous evaluation and release discipline: offline eval sets, continuous monitoring, regression blocking across model/prompt versions
- Effective collaboration with non-technical risk/compliance stakeholders through workflow shadowing and iterative side-by-side output reviews
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