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About
Machine Learning Engineer currently at Webster Bank building an enterprise-scale LLM agent for Temenos Journey Manager/Maestro, using RAG-style multi-stage retrieval with FAISS/Pinecone, hybrid dense+sparse search, and LoRA fine-tuning optimized via NDCG/MAP and A/B testing. Previously handled messy incident/telemetry data at Deuta Werke GmbH with deterministic + fuzzy entity resolution, and has strong production data engineering experience across Spark/Hadoop and Python ETL systems.
Experience
Machine Learning Engineer (AI Systems)Webster Bank
Data ScientistSypnios
Data Scientist (Software Engineer)German Railways (Deuta Werke GmbH)
Data ScientistiFactory3D GmbH
Software DeveloperTemenos India Pvt Ltd.
Graduate Teaching AssistantUniversity of North Texas
Data Scientist InternSolarvibes GmBH
Education
University of North Texasmaster, Advanced Data Analytics (2024)
University of Bordeauxmaster, Image Processing and Computer Vision (2018)
National Institute of Technology, Trichybachelor, Electronics and Communication Engineering (2016)
Key Strengths
Built enterprise-scale LLM agent with multi-stage retrieval, re-ranking, and semantic search
Implemented hybrid search combining dense embeddings with sparse keyword filters for least-privilege context
Fine-tuned embedding/ranking components using LoRA and optimized with NDCG/MAP
Validated search relevance via A/B testing and iterative feedback loops in controlled deployments
Designed entity resolution pipelines using deterministic matching plus fuzzy matching (Levenshtein, Jaro-Winkler)
Built production-grade, modular Python data workflows with SQLAlchemy/FastAPI and multi-source ingestion
Scaled data processing using multiprocessing and big-data stack (Spark, Hadoop/HDFS, Hive/Pig/MapReduce)
Reduced query times by 50% via partitioning and indexing strategies (iFactory3D)
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