Vinay C.
Machine Learning Engineer
He has designed, developed, and deployed large-scale AI systems for anomaly detection, defect monitoring, and document search, leveraging expertise in Python, C++, PyTorch, TensorFlow, and ONNX.
Vinay has trained billion-parameter models, optimized computer vision pipelines for low-power devices, and mentored global machine learning teams, fostering technical excellence and innovation.
He bridges the gap between research and production, combining deep technical expertise with strategic leadership to deliver scalable, high-impact AI solutions that advance organizational capabilities.
Main expertise
Python8 years
Machine Learning8 years
C++8 yearsExperience7
Member of Technical Staff (MTS) Engineer
- Designed and optimized behavioral modeling pipelines for ML operator kernels such as GEMMs with FP32/FP16/BF16 precision and nonlinearities, ensuring accurate system-level validation prior to hardware deployment.
- Enhanced the CARF (Compute Architecture Reference Flow) to improve ONNX graph manipulation, streamlining data extraction, graph partitioning, and workload analysis for architecture simulations.
- Collaborated with hardware architects and compiler teams to evaluate performance bottlenecks and propose optimizations for compute and memory trade-offs.
- Automated testing and benchmarking pipelines to validate architectural improvements against real-world ML workloads.
- Contributed to design discussions on next-generation AI accelerators, aligning software workflows with silicon architecture.
PythonC++Machine Learning
TensorRTTechnical Project Manager (Part-time, alongside MLE role)
- Coordinated cross-functional teams of over 30 engineers and data scientists to align AI initiatives with Intel’s fab operations strategy.
- Established agile workflows—including scrum boards, sprint reviews, and backlog management—to enhance project visibility and reduce delays.
- Defined project roadmaps for anomaly detection, OCR, and computer vision solutions, ensuring on-time delivery of production-ready systems.
- Served as the liaison between stakeholders, engineers, and leadership, translating business requirements into technical milestones.
- Oversaw the deployment of AI solutions across multiple fabs worldwide, managing dependencies and risk mitigation strategies.
- Delivered quarterly executive reports to senior leadership, detailing ROI, adoption metrics, and operational impact.
Jira
Agile
ScrumMachine Learning Engineer
- Built and deployed the Unified Knowledge Platform (UKP)—an AI-powered search system that ingested factory documentation (PDF, Word, Excel, PowerPoint, OCR images, and Intel-specific formats)—improving knowledge retrieval time by 60%.
- Developed custom autoencoder-based anomaly detection models for wafer image data, deployed at scale on Intel’s internal cloud, reducing false negatives in defect detection.
- Created computer vision solutions for real-time defect detection (including laser damage monitoring) with automated alerts, enhancing yield monitoring.
- Designed and delivered a codeless object detection platform, enabling engineers without ML backgrounds to train and deploy models through an intuitive user interface.
- Scaled multiple proofs of concept (OCR, image stitching, visual search) into production systems operating in Intel fabs worldwide.
- Developed and launched Intelidays, a holiday management portal for approximately 6,500 fab employees, fully replacing manual spreadsheet workflows.
- Published and presented two peer-reviewed abstracts at IMEC 2022, accepted at a highly competitive internal conference (1% acceptance rate).
- Served as a technical mentor to junior engineers, guiding them in ML model development, pipeline design, and cloud deployment.
Python
TensorFlow
OpenCV
PyTorchMachine Learning
Mentor (Machine Learning & AI)
- Mentored over 150 students worldwide in AI Nanodegree programs, covering machine learning, deep learning, and computer vision.
- Conducted code reviews, project evaluations, and career guidance to help students meet industry standards.
- Designed personalized learning roadmaps for struggling students, improving graduation rates by 40%.
- Delivered 1:1 sessions and group workshops focused on practical ML deployment and debugging techniques.
- Recognized as a Top Mentor for maintaining high ratings and fostering consistent student success stories.
PythonMachine LearningIncubator Manager (Part-time)
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Supported 8 innovation projects from ideation to prototype, including edge-based video analytics, low-power surveillance, and wearable AI.
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Facilitated hackathons and workshops, fostering a culture of innovation among engineers and researchers.
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Provided technical guidance on deep learning model compression, quantization, and inference optimization.
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Successfully transitioned 3 incubator projects into Intel R&D pipelines, influencing product roadmaps.
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Reported directly to senior architecture leaders on incubation outcomes, technical feasibility, and future directions.
Mentor (AI & Data Science)
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Supported mid-career professionals transitioning into data science and ML roles.
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Provided hands-on guidance in Python, SQL, TensorFlow, and ML pipelines.
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Helped students build portfolio-ready projects, including NLP apps, recommender systems, and computer vision models.
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Conducted mock technical interviews and shared hiring best practices for FAANG-level interviews.
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Improved mentees’ job placement outcomes by accelerating project readiness and interview confidence.
Python
SQLTensorFlowMachine LearningDeep Learning Engineer
- Researched and implemented a Siamese CNN pipeline for relative pose estimation between 3D voxel grids, achieving a 90% reduction in memory usage compared to traditional methods.
- Published research at ICRA 2019 titled “End-to-End Relative Pose Estimation of Point Clouds and Voxel Grids.”
- Developed an object detection system optimized for the Movidius Myriad-X VPU, supporting over 200 object classes at LPIRC 2019, and demonstrated state-of-the-art performance on low-power hardware.
- Engineered a standalone low-power IP camera prototype featuring MIPI capture, JPEG compression, neural inference (via OpenVINO), and HTTP streaming, later used as a reference design for edge AI applications.
- Collaborated with compiler and firmware engineers to ensure neural workloads were efficiently mapped onto Movidius VPUs.
- Contributed to early prototyping of next-generation embedded AI processors, focusing on balancing accuracy, throughput, and power efficiency.
PythonC++TensorFlow
OpenvinoAssessments
Engineering excellence
Vinay’s overall performance in a 90-minute live technical assessment ranks in the top 5% of vetted Machine Learning Engineers at Proxify.
Education
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