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Harideep Balusa
Mid-level AI/ML Engineer specializing in FinTech risk, fraud detection, and GenAI/RAG systems
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About
Built and productionized Azure-based LLM/RAG systems for regulatory/compliance use cases, including automating analyst research and compliance report generation across large unstructured document sets. Demonstrates strong practical depth in hallucination mitigation, hybrid retrieval tuning (BM25 + embeddings), and production MLOps (Databricks, Cognitive Search, AKS, Airflow/MLflow), plus proven ability to deliver auditable, explainable solutions with non-technical compliance teams.
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Key Strengths
- Built and deployed end-to-end LLM RAG system to production for compliance research/reporting
- Strong grounding and hallucination-control approach ('no source, no answer' + confidence scoring)
- Retrieval optimization expertise (hybrid BM25+dense, chunking experimentation) to improve recall/precision
- Production-grade MLOps deployment on Azure (Databricks, Cognitive Search, AKS, containerized API)
- Orchestrated full ML lifecycle pipelines with Airflow (DAGs, SLAs, retries, alerting) and scaled NLP tasks via Kubernetes executor
- Structured evaluation of agents/workflows with KPIs, synthetic/real test cases, load tests, A/B testing, and feedback loops
- Effective collaboration with non-technical regulatory stakeholders; translated requirements into auditable, explainable AI workflows
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