Looks like you have JavaScript disabled. For the full Twine experience, you will need to re-enable it.

I’m Vaishnavi Kaleru, an AI/ML Engineer with 4+ years of experience building large-scale machine learning and deep learning systems. I specialize in transformer-based models, self-supervised learning, RAG pipelines, and multimodal AI for fraud detection, recommendation, and multimedia analysis. I thrive on distributed training, GPU/TPU acceleration, and multi-cloud MLOps, delivering high-performance, low-latency AI solutions with responsible AI practices. I enjoy collaborating across teams to translate complex data into actionable insights that scale globally.…I’m Vaishnavi Kaleru, an AI/ML Engineer with 4+ years of experience building large-scale machine learning and deep learning systems. I specialize in transformer-based models, self-supervised learning, RAG pipelines, and multimodal AI for fraud detection, recommendation, and multimedia analysis. I thrive on distributed training, GPU/TPU acceleration, and multi-cloud MLOps, delivering high-performance, low-latency AI solutions with responsible AI practices. I enjoy collaborating across teams to translate complex data into actionable insights that scale globally.

Vaishnavi Kaleru

AI Engineer, Data Scientist, Data Analyst, +2





I’m Vaishnavi Kaleru, an AI/ML Engineer with 4+ years of experience building large-scale machine learning and deep learning systems. I specialize in transformer-based models, self-supervised learning, RAG pipelines, and multimodal AI for fraud detection, recommendation, and multimedia analysis. I thrive on distributed training, GPU/TPU acceleration, and multi-cloud MLOps, delivering high-performance, low-latency AI solutions with responsible AI practices. I enjoy collaborating across teams to translate complex data into actionable insights that scale globally.…I’m Vaishnavi Kaleru, an AI/ML Engineer with 4+ years of experience building large-scale machine learning and deep learning systems. I specialize in transformer-based models, self-supervised learning, RAG pipelines, and multimodal AI for fraud detection, recommendation, and multimedia analysis. I thrive on distributed training, GPU/TPU acceleration, and multi-cloud MLOps, delivering high-performance, low-latency AI solutions with responsible AI practices. I enjoy collaborating across teams to translate complex data into actionable insights that scale globally.

Available to hire

I’m Vaishnavi Kaleru, an AI/ML Engineer with 4+ years of experience building large-scale machine learning and deep learning systems. I specialize in transformer-based models, self-supervised learning, RAG pipelines, and multimodal AI for fraud detection, recommendation, and multimedia analysis.

I thrive on distributed training, GPU/TPU acceleration, and multi-cloud MLOps, delivering high-performance, low-latency AI solutions with responsible AI practices. I enjoy collaborating across teams to translate complex data into actionable insights that scale globally.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Work Experience

AI/ML Engineer at Stripe

May 1, 2024 - Present

Designed and deployed a transformer-based Payments Foundation Model using self-supervised learning, fine-tuning, and RAG pipelines on billions of transactions, generating embeddings for fraud detection and authorization optimization. Built distributed training pipelines across GPU/TPU clusters on GCP Vertex AI and AWS SageMaker for scalable experimentation. Developed real-time inference microservices with FastAPI, Docker, Kubernetes (GKE & EKS), ONNX Runtime, TorchScript, and Triton Inference Server, reducing latency for fraud scoring. Implemented end-to-end feature pipelines with Spark, PySpark, Airflow, Kafka, Redis Streams, and feature stores for low-latency ML inference. Applied prompt engineering, QLoRA fine-tuning, and multimodal fusion to domain-specific payment data, improving fraud detection accuracy by ~64%. Implemented MLOps pipelines with MLflow, Weights & Biases, Terraform, ArgoCD, and Azure DevOps, including CI/CD, model versioning, canary deployments, rollback strategies

Software Engineer – Machine Learning at Microsoft

September 1, 2023 - October 24, 2025

Designed end-to-end AI pipelines for real-time analysis of music and performance videos using Python, PyTorch, Hugging Face Transformers, and transfer learning with CNN/Transformer backbones, achieving >98% accuracy in detecting musical patterns, rhythm, and performer gestures. Built and maintained large-scale audio/video data pipelines with Python, Apache Spark, Airflow, and Hive/Presto, automating preprocessing for 50,000+ hours of performance recordings. Implemented distributed training workflows on Azure GPU clusters with Docker, Kubernetes, and TorchElastic, reducing model training time by 35% while maintaining state-of-the-art performance across diverse genres. Optimized production-ready multimodal models using ONNX, TensorRT, and Azure ML, achieving sub-100ms latency for real-time music analysis and recommendations on edge/mobile. Applied ML lifecycle management with MLflow, Weights & Biases, and Optuna, enabling reproducible results, hyperparameter tuning, and efficient iterati