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I am a results-driven AI & Machine Learning Engineer with 6+ years of experience designing, deploying, and optimizing large-scale AI systems across technology, finance, and retail domains. I have a strong track record of improving model performance, reducing latency, and delivering measurable business impact through end-to-end ML lifecycle management. I thrive on cross-functional collaboration, mentoring teammates, and building robust, interpretable AI solutions that scale in production. I enjoy staying at the forefront of GPU-accelerated inference, MLOps, and explainability to ensure reliable, compliant, and business-friendly AI systems.…I am a results-driven AI & Machine Learning Engineer with 6+ years of experience designing, deploying, and optimizing large-scale AI systems across technology, finance, and retail domains. I have a strong track record of improving model performance, reducing latency, and delivering measurable business impact through end-to-end ML lifecycle management. I thrive on cross-functional collaboration, mentoring teammates, and building robust, interpretable AI solutions that scale in production. I enjoy staying at the forefront of GPU-accelerated inference, MLOps, and explainability to ensure reliable, compliant, and business-friendly AI systems.

Sudheer Kumar V

AI Engineer, Data Scientist, Developer, +2

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I am a results-driven AI & Machine Learning Engineer with 6+ years of experience designing, deploying, and optimizing large-scale AI systems across technology, finance, and retail domains. I have a strong track record of improving model performance, reducing latency, and delivering measurable business impact through end-to-end ML lifecycle management. I thrive on cross-functional collaboration, mentoring teammates, and building robust, interpretable AI solutions that scale in production. I enjoy staying at the forefront of GPU-accelerated inference, MLOps, and explainability to ensure reliable, compliant, and business-friendly AI systems.…I am a results-driven AI & Machine Learning Engineer with 6+ years of experience designing, deploying, and optimizing large-scale AI systems across technology, finance, and retail domains. I have a strong track record of improving model performance, reducing latency, and delivering measurable business impact through end-to-end ML lifecycle management. I thrive on cross-functional collaboration, mentoring teammates, and building robust, interpretable AI solutions that scale in production. I enjoy staying at the forefront of GPU-accelerated inference, MLOps, and explainability to ensure reliable, compliant, and business-friendly AI systems.

Available to hire

I am a results-driven AI & Machine Learning Engineer with 6+ years of experience designing, deploying, and optimizing large-scale AI systems across technology, finance, and retail domains. I have a strong track record of improving model performance, reducing latency, and delivering measurable business impact through end-to-end ML lifecycle management.

I thrive on cross-functional collaboration, mentoring teammates, and building robust, interpretable AI solutions that scale in production. I enjoy staying at the forefront of GPU-accelerated inference, MLOps, and explainability to ensure reliable, compliant, and business-friendly AI systems.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Language

English

Fluent

Work Experience

AI Engineer at NVIDIA

July 1, 2024 - November 26, 2025

Designed and deployed a CNN-based object detection model using PyTorch on NVIDIA GPUs, optimizing inference with TensorRT and ONNX to achieve 30% lower latency and 15% higher accuracy. Built a large-scale data ingestion pipeline with Apache Spark, AWS S3, and Delta Lake to support 10M+ labeled images for real-time model training. Implemented automated Kubeflow workflows integrated with Jenkins CI/CD, Docker, and Kubernetes for seamless retraining and deployment across GPU clusters. Optimized GPU memory allocation with hardware/software teams, boosting training throughput by 25% and improving large-batch performance. Integrated detection outputs into NVIDIA Omniverse to accelerate simulation-driven validation and reduce synthetic data generation time by 40%. Established ML monitoring with MLflow, Prometheus, and Grafana, maintaining 95%+ accuracy with adaptive retraining triggers. Applied SHAP-based explainability and Albumentations/OpenCV augmentations to improve generalization across

Machine Learning Engineer at Accenture

November 30, 2022 - November 30, 2022

Led end-to-end fraud detection system development using Python, XGBoost, and Scikit-Learn, processing 500M+ transactions to detect anomalies in real time with 95% precision. Engineered and deployed a distributed Apache Spark pipeline on Azure Databricks and Kafka for streaming ingestion and near-real-time scoring of banking transactions. Built automated MLOps workflows with Docker, GitHub Actions, and Azure ML, enabling 50% faster deployments and improved model retraining traceability. Increased fraud detection accuracy by 10% and reduced false positives by 30%, generating $2M+ annual savings. Delivered an interactive Power BI dashboard with SHAP explanations for regulatory transparency. Developed a demand forecasting pipeline using PyTorch LSTM and PySpark on AWS EMR, improving forecast accuracy by 18% and reducing stockouts by 20% and overstocks by 12% across 200+ retail locations, saving $1.5M annually. Automated retraining and deployment with Airflow + Azure ML for daily updates an