Headline

Applied Scientist | Arizona State University | Creator of AutoTimm & NightFlow

About

As a former Applied Scientist II at SFL, I contributed to the development of advanced machine learning solutions by managing the end-to-end training lifecycle for sports video processing and designing scalable ML inference pipelines using AWS technologies. Collaborating with teams, I implemented and benchmarked multi-object tracking algorithms to address challenges like occlusions and diverse camera angles, enhancing detection accuracy and player identification. Holding a Master’s degree in Computer Science from Arizona State University, I specialize in Python, PyTorch, and deep learning. My prior roles at Lytx and Mareana enabled me to work on impactful computer vision and AI projects, such as weather classification models, driver fatigue detection, and dataset curation using active learning techniques. I am driven to advance innovative AI solutions for addressing real-world challenges in various industries.

Location

Bengaluru

AI stack

codex

opencode

claudecode

Work experience

Personal goal pursuit at Career Break, Jan 2026 - Present · 8 mos, Bengaluru, Karnataka, India, - Pursuing an independent Applied AI initiative that extends Computer Vision and Deep Learning expertise into Multimodal Foundation Models, Vision-Language-Action (VLA) systems, and Embodied AI. - Building an end-to-end VLA project spanning multimodal perception, dataset preparation, LoRA/QLoRA fine-tuning, evaluation, and deployment-focused optimization. - Developing practical Generative AI capabilities across vision-language models, diffusion-based synthetic-data generation, and robotics perception—including detection, segmentation, depth estimation, and scene understanding. - Strengthening production ML skills through quantization, compression, ONNX/TensorRT acceleration, latency benchmarking, and edge-inference deployment. - Documenting reproducible implementations, benchmarks, technical write-ups, and demos that translate AI research concepts into deployable systems.

Applied Scientist II at SFL, Oct 2025 - Jan 2026 · 4 mos, Bengaluru, - Owned the end-to-end training lifecycle, including processing raw sports videos, generating and validating annotations using AWS Ground Truth, and fine-tuning and evaluating YOLO-v9, RT-DETR, and Grounding DINO to improve detection accuracy under diverse camera angles, occlusions, and lighting conditions. - Applied and benchmarked multi-object tracking algorithms using BoxMot to maintain consistent player identities in highly occluded and crowded scenes, analyzing trade-offs between ID stability and computational cost. - Designed and deployed scalable ML inference pipelines on AWS using ECS for service orchestration, Amazon ECR for containerized model artifacts, SageMaker for managed, asynchronous model serving, and MLflow for experiment tracking and model lifecycle management.

Machine Learning Engineer II at Lytx, Inc., Dec 2023 - Jul 2025 · 1 yr 8 mos, Bengaluru, - Conducted a comprehensive literature review, developed and trained a model from scratch, tested and deployed weather classification models, achieving >98% precision and recall, enhancing visual condition recognition for edge-based safety systems. - Applied research-driven active learning methods (e.g., Influence Selection) to reduce data labeling effort by 40%, while maintaining >90% across all key metrics (accuracy, precision, recall, F1). - Leveraged distillation and representation learning models such as DINOv2 and CLIP , integrated with ScaNN and One-vs-All retrieval, to automate dataset curation for weakly labeled and unlabeled data. - Led end-to-end research and development of a driver fatigue detection module using a MobileNetV2 + GRU architecture; achieved 94%+ precision/recall for real-time yawn detection across diverse lighting and camera angles. - Architected distributed training infrastructure across multi-node, multi-GPU clusters to scale training on 10B+ image datasets, enabling the development of proprietary vision models. - Accelerated model development cycle by 15x by converting legacy Keras/TensorFlow pipelines into modular PyTorch Lightning framework, significantly improving reproducibility and maintainability. - Routinely worked with AWS infrastructure (S3, EC2, SageMaker, EKS, ECR) to support scalable training, deployment, and monitoring of production models.

Data Scientist at Mareana, Inc., Jul 2021 - Jul 2022 · 1 yr 1 mo, Bengaluru, - Developed a pytorch based framework with configurable training pipelines for object detection, segmentation, and vision transformers within Mareana's Manufacturing Intelligence Hub, reducing manual setup time by 70%. - Researched and trained a transformer-based model to convert chemical equation images into corresponding formulas. - Applied self-supervised learning techniques (DINO, Segment Anything) to reduce annotation cost by 40%, accelerating dataset preparation for downstream tasks. - Optimized model inference latency by 3x by converting TorchScript models to TensorRT via ONNX, enabling real-time performance in production environments. - Developed and maintained RESTful APIs using Flask and FastAPI for seamless integration of ML models into web-based applications and internal tools. - Mentored junior ML engineers, guiding them through complete ML pipeline development including data acquisition, annotation (Labelbox), model prototyping, evaluation, and deployment best practices.

Data Scientist at WayCool Foods, Aug 2019 - Jul 2021 · 2 yrs, Bengaluru

Education

Master's degree, Computer Science, Arizona State University

Bachelor’s Degree, Computer Science, Visvesvaraya Technological University