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Sai Addala
Mid-level AI/ML Engineer specializing in financial risk, fraud analytics, and forecasting
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About
Built and productionized an LLM-powered financial intelligence and forecasting platform at Northern Trust using a RAG architecture (LangChain + Hugging Face + FAISS) with end-to-end MLOps (Docker/Kubernetes, Airflow, MLflow). Emphasized regulatory-grade explainability (SHAP/Power BI) and hallucination control (retrieval-only grounding), achieving ~30% forecasting accuracy improvement and ~65% reduction in analyst research time, with sub-second inference and 95% uptime on EKS/AKS.
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Key Strengths
- Built and deployed production LLM/RAG platform for financial risk/portfolio users
- Delivered measurable business impact: ~30% increase in portfolio forecasting accuracy
- Delivered measurable productivity gains: ~65% reduction in analyst manual research
- Strong hallucination-mitigation approach (retrieval-only grounding, similarity thresholds, 'no answer' fallback, offline eval)
- Production MLOps and orchestration at scale (Dockerized microservices on Kubernetes with Airflow/MLflow)
- High-reliability, low-latency deployment experience (95% uptime, sub-second inference) using autoscaling and blue-green deployments
- Rigorous evaluation methodology (adversarial + unit/integration tests, A/B testing, drift monitoring, automated retraining)
- Effective collaboration with non-technical stakeholders; translated needs into explainable dashboards and NL query workflows
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