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Thrinesh Thode
Mid-level AI/ML Engineer specializing in MLOps and LLM applications
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About
BNY Mellon engineer who has built and operated production AI systems end-to-end: a LangChain/Pinecone RAG platform scaled via FastAPI + Kubernetes to 1000 RPM with 99.9% uptime, supported by monitoring and data-drift detection. Also deep in data/infra orchestration (Airflow, Dagster, Terraform on AWS/EMR/EC2), processing 500GB+ daily and delivering measurable reliability and performance gains, plus strong compliance-facing model explainability using SHAP and Tableau.
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Key Strengths
- Architected and deployed production RAG system (LangChain + Pinecone) for real-time document querying
- Scaled FastAPI service to 1000 requests/min with Kubernetes horizontal autoscaling while maintaining 99.9% uptime
- Implemented automated monitoring, data drift detection, and self-healing workflows to prevent user impact
- Operated high-volume systems: 10,000+ daily queries and 500GB+ daily data processing
- Built orchestration pipelines with Airflow and Dagster; reduced pipeline processing time by 30%
- Improved pipeline reliability by 40% via Terraform + Dagster sensors (retries, cleanup, drift detection)
- Designed measurable AI workflow evaluation using A/B testing and real-time metric monitoring
- Strong stakeholder communication: explained credit risk model drivers with SHAP and delivered exec-ready Tableau dashboards
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