Mahalakshmi Vishumolakala
Ml Software Engineer @JPMorganChase
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WORK HISTORY
Ml Software Engineer @JPMorganChase
US
Deployed real-time fraud detection models using PyTorch, Scikit-learn, and AWS SageMaker, improving fraud detection accuracy by 23% and reducing false positives across 10M+ daily transactions. • Engineered scalable ML pipelines with Apache Spark, Hadoop, and Kafka, cutting data preparation time by 35% and enabling faster model retraining cycles. • Developed and fine-tuned large language models (BERT, GPT) with Hugging Face, achieving 92% classification accuracy on compliance documents and reducing manual review workload by 40%. • Optimized inference performance by deploying models with NVIDIA Triton Inference Server on Kubernetes, reducing latency by 45% and supporting high-volume financial transactions in real time. • Implemented MLOps best practices with MLflow, Docker, and CI/CD pipelines, cutting model deployment cycles by 30% and ensuring governance compliance through automated versioning and drift detection.
EDUCATION
Osmania University
Bachelor of Engineering - BE
University of North Carolina at Charlotte
Master of Engineering - MEng
ABOUT MAHALAKSHMI VISHUMOLAKALA
Software Engineer with 4+ years of experience building scalable, high-performance applications and distributed data systems across finance, healthcare, and enterprise domains. Proficient in full-stack development (Python, Java, C++, SQL), cloud platforms (AWS, Azure, GCP), and integrating machine learning solutions (PyTorch, TensorFlow, Hugging Face) with MLOps frameworks (Docker, Kubernetes, MLflow, Triton) to deliver secure, reliable, and production-ready systems at scale.
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