Available to hire
Hi, I’m Arnav Anand. I’m an AI/ML Engineer with 3+ years of experience designing and deploying deep learning and computer vision solutions across advertising, robotics, and video analytics. I enjoy turning complex data into practical, high-impact models and scalable systems.
Across my projects, I work hands-on with PyTorch, TensorFlow, and Apache Spark, and I’m proficient in MLOps, Docker, Kubernetes, MLflow, and AWS SageMaker for production deployment and monitoring. I value collaboration with cross-functional teams to deliver reliable, data-driven results.
Skills
Experience Level
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Intermediate
Intermediate
Language
English
Fluent
Work Experience
AI / ML Engineer at Epsilon
August 1, 2025 - November 26, 2025Built and productionized large-scale data pipelines using PyTorch, TensorFlow, and Apache Spark to process over 15 million ad interaction records monthly for real-time personalization and campaign analytics. Orchestrated scalable ML inference services with Docker, Kubernetes, and AWS SageMaker, reducing model refresh latency by 12 hours and improving reliability for high-traffic advertising systems. Automated MLOps workflows with MLflow and Nvidia Triton, enabling reproducible experiments across multi-region AWS deployments. Implemented system monitoring dashboards with Prometheus, Grafana, and Weights & Biases to ensure 24/7 uptime and strong cross-team collaboration.
Computer Vision Intern at Gritt Robotics
December 1, 2024 - December 1, 2024Fine-tuned Detectron2 instance segmentation models (Mask R-CNN & PoinTrend) on synthetic and real-world data (20K+ labeled samples) to enhance forklift perception and scene segmentation accuracy. Tuned inference parameters via grid search for score and NMS thresholds, increasing per-class mean Average Precision (mAP) by 15 points and stabilizing live detection streams across test environments. Deployed calibrated multi-camera feeds into the inference pipeline on an in-house embedded device, sustaining 28 FPS with latency tracking and performance monitoring. Documented calibration and evaluation SOPs and developed visual benchmarking utilities using OpenCV and Matplotlib for reproducible model evaluations and data quality checks.
Computer Vision Engineer at Wobot Intelligence
May 1, 2023 - May 1, 2023Architected real-time video analytics pipelines using TensorFlow, PyTorch, and OpenCV, processing over 500 concurrent camera streams and enhancing model stability for enterprise deployments. Implemented ML model deployments on AWS EKS with Docker and Kubernetes, integrating automated CI/CD workflows to accelerate release cycles by 4 days per sprint. Optimized inference performance using TensorRT, ONNX, and GPU acceleration, achieving sub-120 ms latency for live detection and ROI breach use cases. Orchestrated system monitoring with Prometheus, Grafana, and Loki, enabling proactive drift detection and reducing troubleshooting time from 3 hours to 20 minutes. Developed 15+ computer vision use-cases from scratch for object tracking, pose estimation, and ROI analytics, supporting AI-driven safety and compliance solutions across retail and manufacturing clients. Drove collaboration between AI, DevOps, and product teams to standardize MLOps workflows for model versioning and experiment track
Computer Vision Engineer at Wobot Intelligence
March 1, 2021 - May 1, 2023Architected real-time video analytics pipelines using TensorFlow, PyTorch, and OpenCV, processing over 500 concurrent camera streams and enhancing model stability for enterprise deployments. Implemented ML model deployments on AWS EKS with Docker and Kubernetes, integrating automated CI/CD workflows to accelerate release cycles by 4 days per sprint through close coordination with DevOps. Optimized inference performance using TensorRT, ONNX, and GPU acceleration, achieving sub-120 ms latency for live detection and polygon ROI breach use cases. Orchestrated system monitoring with Prometheus, Grafana, and Loki, enabling proactive drift detection and reducing troubleshooting time from 3 hours to 20 minutes. Developed over 15 computer vision use-cases from scratch for object tracking, pose estimation, and ROI analytics, supporting AI-driven safety and compliance solutions across retail and manufacturing clients. Directed collaboration between AI, DevOps, and product teams to standardize MLO
ML Engineer at Dell Technologies
February 1, 2021 - February 1, 2021Built and trained 7+ deep learning models using TensorFlow, PyTorch, and Scikit-learn to classify and detect images across multiple datasets, improving average model accuracy by 12–15% through data augmentation and tuning. Implemented computer vision models (YOLOv5, Faster R-CNN) for object detection tasks, reducing false positives by 18% and enabling faster inference on 10,000+ image samples. Utilized AWS SageMaker to run model training and evaluation on GPU-backed instances, accelerating experimentation cycles by 30% compared to local runs. Developed data preprocessing and feature extraction pipelines using Python, NumPy, and OpenCV, cleaning and standardizing 50K+ images for model training. Collaborated with data scientists to log experiments and visualize results in MLflow, ensuring reproducibility across 20+ model iterations and maintaining versioned experiment tracking.
ML Engineer at Dell Technologies
June 1, 2020 - February 1, 2021Built and trained 7+ deep learning models using TensorFlow, PyTorch, and Scikit-learn to classify and detect images across multiple datasets, improving average model accuracy by 12–15% through data augmentation and tuning. Implemented computer vision models (YOLOv5, Faster R-CNN) for object detection tasks, reducing false positives by 18% and enabling faster inference on 10,000+ image samples. Utilized AWS SageMaker to run model training and evaluation on GPU-backed instances, accelerating experimentation cycles by 30% compared to local runs. Developed data preprocessing and feature extraction pipelines using Python, NumPy, and OpenCV, cleaning and standardizing 50K+ images for model training. Collaborated with data scientists to log experiments and visualize results in MLflow, ensuring reproducibility across 20+ model iterations and maintaining versioned experiment tracking.
Education
Master of Science in Computer Engineering at The University of Texas at Dallas
August 1, 2023 - May 1, 2025Master of Science in Computer Engineering at The University of Texas at Dallas
January 11, 2030 - December 19, 2025Master of Science in Computer Engineering at The University of Texas at Dallas
August 1, 2023 - May 1, 2025Master of Science in Computer Engineering at The University of Texas at Dallas
August 1, 2023 - May 1, 2025Qualifications
Industry Experience
Software & Internet, Media & Entertainment, Professional Services, Computers & Electronics
Skills
Experience Level
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Intermediate
Intermediate
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