From ae48107e065453349220b0a62e30f469e028303d Mon Sep 17 00:00:00 2001 From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com> Date: Thu, 12 Feb 2026 05:45:01 +0000 Subject: [PATCH 1/3] Initial plan From d268934850d51af7b4f4d744c77f650d65ee136b Mon Sep 17 00:00:00 2001 From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com> Date: Thu, 12 Feb 2026 05:48:03 +0000 Subject: [PATCH 2/3] Add complete content to resume_deep_gen.html file Co-authored-by: iRonJ <6108031+iRonJ@users.noreply.github.com> --- _pages/resume_deep_gen.html | 242 +++++++++++++++++++++++++++++++++++- 1 file changed, 241 insertions(+), 1 deletion(-) diff --git a/_pages/resume_deep_gen.html b/_pages/resume_deep_gen.html index 9ab0a48..1db1506 100644 --- a/_pages/resume_deep_gen.html +++ b/_pages/resume_deep_gen.html @@ -17,7 +17,247 @@ Ron Jailall - Resume + + + + - + + + +
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+ To Save as PDF: Press Ctrl + P (Windows) or Cmd + P (Mac). +
Select "Save as PDF" as the destination. This blue box will disappear automatically in the PDF. +

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Ron Jailall

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Deep Learning & Generative AI Engineer

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Raleigh, NC | (608) 332-8605

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rojailal@gmail.com

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https://ironj.github.io/

+
+
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+ + +
+

Professional Profile

+

+ Deep Learning & Generative AI Engineer with 15+ years of experience building and deploying neural network solutions from research to production. Expert in Transformer architectures, Diffusion Models, and Large Language Models, with deep hands-on experience in PyTorch and TensorFlow. Proven track record of owning the complete ML lifecycle—from dataset curation and model training to optimization and deployment at scale. Specializes in rapid prototyping, model fine-tuning, and bridging the gap between cutting-edge research and real-world applications. +

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+ + +
+

Technical Skills

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+ Deep Learning Architectures: + Transformers (GPT, Llama, BERT), Vision Transformers (ViT), Diffusion Models (Stable Diffusion, DDPM), CNNs (ResNet, MobileNet, U-Net). +
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+ Generative AI: + LLM Fine-Tuning (LoRA, PEFT, Prefix-Tuning), Prompt Engineering, Text-to-Image Generation, Image Segmentation & Matting. +
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+ ML Frameworks & Tools: + PyTorch, TensorFlow 2/Keras, Hugging Face Transformers, ONNX, TensorRT, Weights & Biases, Ray. +
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+ Engineering & DevOps: + Python, C++, Docker, Kubernetes, AWS (Sagemaker, Lambda, Batch), GCP (Vertex AI), CI/CD (GitLab, Terraform). +
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+
+ + +
+

Professional Experience

+ + +
+
+

ML Engineering Consultant / Technical Lead

+ 2024 – Present +
+
Remote
+

Delivering end-to-end deep learning solutions for diverse clients, from research prototypes to production systems.

+
    +
  • Computer Vision (Portrait Matting Project): Owned the complete lifecycle of a custom human matting model, from dataset selection (P3M-10k) to production deployment. Architected a MobileNetV2-based U-Net in TensorFlow 2/Keras, implementing advanced data augmentation pipelines to handle diverse lighting and background conditions. Exported to ONNX for cross-platform inference, achieving real-time performance on CPU.
    • +
  • Generative AI & Diffusion Models: Optimized Stable Diffusion models for low-latency inference in video pipelines, implementing techniques like attention slicing and mixed-precision inference. Built production-ready inference servers for text-to-image generation, handling dynamic batching and GPU memory management.
    • +
  • LLM & Transformer Optimization: Migrated and optimized Nvidia Riva (transformer-based ASR/NLP) microservices to AWS, implementing efficient batching and model quantization strategies. Fine-tuned open-source LLMs (Llama, Mistral) for domain-specific tasks, utilizing parameter-efficient methods like LoRA.
    • +
  • RAG & Embeddings: Designed and deployed vector embedding pipelines for semantic search and recommendation systems, leveraging sentence transformers and FAISS for efficient similarity search at scale.
    • +
+
+ + +
+
+

Lead Engineer, AI R&D

+ 2023 – 2024 +
+
Vidable.ai | Remote
+

Led R&D team in evaluating and deploying cutting-edge generative models for production applications.

+
    +
  • LLM Research & Deployment: Evaluated latest LLM architectures (GPT-4, Claude, Llama family) for production use cases. Modified C++ inference engines (llama.cpp) to support custom quantization schemes, achieving 4-bit inference with minimal quality degradation.
    • +
  • Generative Model Prototyping: Built rapid prototypes combining LLMs with diffusion models, creating end-to-end generative workflows. Implemented prompt engineering strategies and few-shot learning techniques to maximize model performance without expensive fine-tuning.
    • +
  • Infrastructure & MLOps: Designed CI/CD pipelines (Terraform/AWS) for deploying ML inference endpoints, implementing auto-scaling and monitoring. Created model evaluation frameworks to assess generation quality, prompt robustness, and model drift.
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+
+ + +
+
+

Lead Engineer

+ 2014 – 2023 +
+
Sonic Foundry | Remote
+

Pioneered deep learning initiatives, introducing neural networks and transformer models to the organization.

+
    +
  • Neural Network Training & Fine-Tuning: Built and trained U-Nets in PyTorch for image segmentation tasks on AWS Sagemaker. Developed prefix-tuning datasets for Nvidia Megatron LLM (early PEFT exploration), optimizing for domain-specific language generation.
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  • Computer Vision Research: Prototyped neural search of video archives using Vision Transformers and CLIP-like models in PyTorch/TensorFlow. Implemented video segmentation and classification pipelines, significantly improving content discoverability.
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  • NLP & Speech: Created custom NLP preprocessing pipelines (TF-IDF, stemming) to prepare sentence corpuses for speech recognition model training with IBM Watson. Built text classification models for automated video metadata tagging.
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  • ML Infrastructure: Founded internal AI research group, leading hackathons focused on applying transformers and generative models to core products. Deployed GGML-based Llama models to AWS Lambda for cost-effective serverless inference.
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+
+
+ + +
+

Selected Technical Talks & Research

+
+
+
+

Hyperfast AI: Rethinking Design for 1000 tokens/s

+ AI Tinkerers Raleigh, Dec 2025 +
+
    +
  • Explored the implications of ultra-fast transformer inference for generative AI applications, focusing on new UX patterns enabled by near-instant token generation.
    • +
+
+
+
+

Apple's On-Device VLM: The Future of Multimodal AI

+ Conference Talk, Sep 2025 +
+
    +
  • Technical analysis of Vision-Language Models optimized for edge deployment, covering quantization techniques and architectural innovations for mobile Vision Transformers.
    • +
+
+
+
+

Cerebras OS: Operating Systems for AI

+ Technical Blog Post, May 2025 +
+
    +
  • Deep dive into specialized hardware accelerators for transformer training and inference, analyzing architectural trade-offs in custom silicon design.
    • +
+
+
+
+ + +
+

Selected Projects & Competitions

+
+
+ Cohere Hackathon (3rd Place): Built an LLM-powered presentation assistant using real-time transcription and retrieval-augmented generation, demonstrating advanced prompt engineering and context management techniques. +
+
+ Gaussian Splatting Experiments: Explored novel 3D representation learning through Gaussian Splatting, implementing custom training loops in PyTorch for real-time neural rendering. +
+
+ Open Source Contributions: Active contributor to Hugging Face Transformers ecosystem, with experience debugging and optimizing transformer model implementations. +
+
+
+ + +
+

Education & Certifications

+
+
+ NC State University | Electrical & Computer Engineering (Completed 75 Credit Hours) +
+
+ Coursera Verified Certificates: +
    +
  • Neural Networks for Machine Learning (Geoffrey Hinton) | ID: 3MJACUGZ4LMA
    • +
  • Image and Video Processing | ID: E9JX646TTS
    • +
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+ From 78dbe3bfacf838a4dbe3fdf0f2fd56093bef2add Mon Sep 17 00:00:00 2001 From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com> Date: Thu, 12 Feb 2026 05:51:20 +0000 Subject: [PATCH 3/3] Update resume_deep_gen.html with Applied Research Engineer focus Co-authored-by: iRonJ <6108031+iRonJ@users.noreply.github.com> --- _pages/resume_deep_gen.html | 151 +++++++++++++++--------------------- 1 file changed, 63 insertions(+), 88 deletions(-) diff --git a/_pages/resume_deep_gen.html b/_pages/resume_deep_gen.html index 1db1506..bef38e6 100644 --- a/_pages/resume_deep_gen.html +++ b/_pages/resume_deep_gen.html @@ -5,15 +5,6 @@ - - - Ron Jailall - Resume @@ -97,7 +88,7 @@

Ron Jailall

-

Deep Learning & Generative AI Engineer

+

Applied Research Engineer

Raleigh, NC | (608) 332-8605

@@ -109,35 +100,72 @@

Deep Learning & Generative
-

Professional Profile

+

Profile

- Deep Learning & Generative AI Engineer with 15+ years of experience building and deploying neural network solutions from research to production. Expert in Transformer architectures, Diffusion Models, and Large Language Models, with deep hands-on experience in PyTorch and TensorFlow. Proven track record of owning the complete ML lifecycle—from dataset curation and model training to optimization and deployment at scale. Specializes in rapid prototyping, model fine-tuning, and bridging the gap between cutting-edge research and real-world applications. + Engineer with 15+ years of experience bridging the gap between unsolved challenges and high-impact industry applications. Expert in Volumetric World Models (Gaussian Splatting), Generative Media, and Efficient Inference. Proven ability to thrive in ambiguity, rapidly prototyping novel solutions for Video Understanding and Multimodal AI while optimizing for deployment on resource-constrained hardware. Deeply experienced in the full ML lifecycle—from dataset curation and model training to performance optimization and production.

- +
-

Technical Skills

+

Core Competencies

- Deep Learning Architectures: - Transformers (GPT, Llama, BERT), Vision Transformers (ViT), Diffusion Models (Stable Diffusion, DDPM), CNNs (ResNet, MobileNet, U-Net). + Generative & Volumetric AI: + Diffusion Models, Gaussian Splatting (3D World Representations), NeRF concepts, Video Generation pipelines.
- Generative AI: - LLM Fine-Tuning (LoRA, PEFT, Prefix-Tuning), Prompt Engineering, Text-to-Image Generation, Image Segmentation & Matting. + Multimodal Understanding: + Vision Language Models (VLMs), Video Neural Search, Audio-Visual alignment, RAG pipelines.
- ML Frameworks & Tools: - PyTorch, TensorFlow 2/Keras, Hugging Face Transformers, ONNX, TensorRT, Weights & Biases, Ray. + Model Optimization: + Efficient Inference (TensorRT, ONNX, CoreML), Quantization, Knowledge Distillation, MobileNet/Edge architectures.
- Engineering & DevOps: - Python, C++, Docker, Kubernetes, AWS (Sagemaker, Lambda, Batch), GCP (Vertex AI), CI/CD (GitLab, Terraform). + Frameworks & Engineering: + TensorFlow 2, PyTorch, JAX concepts, Python, C++, CUDA, Metal Shading Language.
+ +
+

Research & Technical Highlights

+ +
+
+

Volumetric World Representation & Physics Simulation (VisionOS Project)

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+
    +
  • Physics-Aware World Modeling: Designed and implemented a volumetric renderer on VisionOS that assigns physical properties ("jiggle physics") to learned 3D Gaussian representations.
    • +
  • Research Application: Demonstrated core World Model principles by enabling static 3D reconstructions to react dynamically to environmental stimuli, simulating cause-and-effect within a learned volumetric space.
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  • Efficient Inference: Optimized the rendering pipeline using custom Metal compute shaders to achieve real-time performance on mobile hardware, validating the feasibility of interactive volumetric video.
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+
+ +
+
+

End-to-End Generative Media Pipeline (Matte Model)

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+
    +
  • Dataset Curation to Deployment: Managed the full lifecycle of a human matting research project. Curated and augmented the P3M-10k dataset to improve robustness against diverse lighting conditions.
    • +
  • Model Training & Architecture: Trained a custom MobileNetV2-based architecture using TensorFlow 2, optimizing the backbone for CPU-efficient video processing.
    • +
  • Performance Optimization: Engineered the inference pipeline to run locally on consumer hardware via ONNX Runtime, replacing heavy cloud-dependent SDKs with a low-latency edge solution.
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+
+ +
+
+

Video Understanding & Diffusion

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+
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  • Controlled Media Generation: Accelerated Stable Diffusion models for real-time thumbnail generation and webcam re-rendering pipelines, reducing latency for live interactive video applications.
    • +
  • Video Neural Search: Prototyped neural search algorithms for massive video archives at Sonic Foundry, utilizing segmentation and classification models to enable semantic understanding of unstructured video data.
    • +
+
+
+

Professional Experience

@@ -145,16 +173,15 @@

-

ML Engineering Consultant / Technical Lead

+

ML Engineering Consultant / Applied Research Engineer

2024 – Present
Remote
-

Delivering end-to-end deep learning solutions for diverse clients, from research prototypes to production systems.

+

Executing applied research and prototyping for diverse clients in GenAI and Computer Vision.

    -
  • Computer Vision (Portrait Matting Project): Owned the complete lifecycle of a custom human matting model, from dataset selection (P3M-10k) to production deployment. Architected a MobileNetV2-based U-Net in TensorFlow 2/Keras, implementing advanced data augmentation pipelines to handle diverse lighting and background conditions. Exported to ONNX for cross-platform inference, achieving real-time performance on CPU.
    • -
  • Generative AI & Diffusion Models: Optimized Stable Diffusion models for low-latency inference in video pipelines, implementing techniques like attention slicing and mixed-precision inference. Built production-ready inference servers for text-to-image generation, handling dynamic batching and GPU memory management.
    • -
  • LLM & Transformer Optimization: Migrated and optimized Nvidia Riva (transformer-based ASR/NLP) microservices to AWS, implementing efficient batching and model quantization strategies. Fine-tuned open-source LLMs (Llama, Mistral) for domain-specific tasks, utilizing parameter-efficient methods like LoRA.
    • -
  • RAG & Embeddings: Designed and deployed vector embedding pipelines for semantic search and recommendation systems, leveraging sentence transformers and FAISS for efficient similarity search at scale.
    • +
  • Prototyping in Ambiguity: Rapidly validated and iterated on novel AI architectures, including high-speed agentic workflows (>1000 tokens/s) that require dynamic replanning and context management.
    • +
  • Multimodal Evaluations: Authored technical research on On-Device VLMs, evaluating the trade-offs between model size, quantization accuracy, and memory bandwidth for multimodal understanding on edge devices.
    • +
  • Hardware Optimization: Optimized Computer Vision models for Nvidia Jetson platforms using TensorRT, enabling real-time multi-view tracking and sensor fusion in resource-constrained environments.

@@ -165,11 +192,11 @@

Lead Engineer, AI R&D

2023 – 2024
Vidable.ai | Remote
-

Led R&D team in evaluating and deploying cutting-edge generative models for production applications.

+

Led the R&D function, evaluating and implementing cutting-edge Generative Media models.

@@ -180,72 +207,20 @@

Lead Engineer

2014 – 2023
Sonic Foundry | Remote
-

Pioneered deep learning initiatives, introducing neural networks and transformer models to the organization.

+

Engineering leadership focused on large-scale video processing and data pipelines.

- -
-

Selected Technical Talks & Research

-
-
-
-

Hyperfast AI: Rethinking Design for 1000 tokens/s

- AI Tinkerers Raleigh, Dec 2025 -
-
    -
  • Explored the implications of ultra-fast transformer inference for generative AI applications, focusing on new UX patterns enabled by near-instant token generation.
    • -
-
-
-
-

Apple's On-Device VLM: The Future of Multimodal AI

- Conference Talk, Sep 2025 -
-
    -
  • Technical analysis of Vision-Language Models optimized for edge deployment, covering quantization techniques and architectural innovations for mobile Vision Transformers.
    • -
-
-
-
-

Cerebras OS: Operating Systems for AI

- Technical Blog Post, May 2025 -
-
    -
  • Deep dive into specialized hardware accelerators for transformer training and inference, analyzing architectural trade-offs in custom silicon design.
    • -
-
-
-
- - -
-

Selected Projects & Competitions

-
-
- Cohere Hackathon (3rd Place): Built an LLM-powered presentation assistant using real-time transcription and retrieval-augmented generation, demonstrating advanced prompt engineering and context management techniques. -
-
- Gaussian Splatting Experiments: Explored novel 3D representation learning through Gaussian Splatting, implementing custom training loops in PyTorch for real-time neural rendering. -
-
- Open Source Contributions: Active contributor to Hugging Face Transformers ecosystem, with experience debugging and optimizing transformer model implementations. -
-
-
-

Education & Certifications

- NC State University | Electrical & Computer Engineering (Completed 75 Credit Hours) + NC State University | Electrical & Computer Engineering (75 Credit Hours)
Coursera Verified Certificates: