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Verified Expert in Engineering
Artificial Intelligence Developer
Paraparaumu, Wellington, New Zealand
Toptal member since December 1, 2025
Bio
Gavin is an AI engineer with a master's degree in AI and 4+ years of professional experience delivering AI products for companies like Crimson Education. He specializes in production-ready NLP, agentic AI, computer vision, and full-stack AI systems, focusing on measurable business impact and scalable deployment on AWS and Azure. Gavin builds state-of-the-art models, designs secure, compliant infrastructure, and integrates models into applications through robust APIs and CI/CD workflows.
Portfolio
Toptal
Artificial Intelligence (AI), Python, Machine Learning...
Crimson Education
Python, PyTorch, FastAPI, Docker, CI/CD Pipelines, GitHub Actions...
Crimson Education
Python, FastAPI, OpenCV, Arduino, PyTorch, Keras, TensorFlow, TypeScript, React...
Experience
- Python - 6 years
- PyTorch - 5 years
- Docker - 4 years
- Computer Vision - 4 years
- Natural Language Processing (NLP) - 4 years
- Next.js - 3 years
- TypeScript - 3 years
- Amazon Web Services (AWS) - 2 years
Work Experience
AI/ML Engineer
2026 - 2026
Toptal
- Built a React/Node.js simulation environment and automated scenario-testing system for AI-powered iOS features on Supabase and Vercel, reducing development cycles by 58% and manual testing time by 69% across multi-agent neuroscience pipelines.
- Built a 3-agent extraction pipeline (extract, reconcile, QA) with batch processing, temporal decay, beyond-window context, relationship extraction, Haiku reconciler, cross-batch persistence, and multi-layer caching for intelligent user profiling.
- Optimized multi-agent pipelines, including personalized notifications leveraging Groq and a custom prompt orchestration platform, reducing token usage by 26% while increasing processing speed 47%.
- Built conversational AI eval platform: GitHub Actions CI/CD on prompt-file changes, LLM-as-judge scoring, ephemeral per-run test-user provisioning, PR preview evals with automated annotations, and parallelized matrix jobs across environments.
- Engineered an eval metrics dashboard with run history, TTFT charts (p5/p50 percentiles), quality-score trend lines, multi-model comparison, a vs-Prod compare tab, and RBAC-gated schedule management.
- Hardened Supabase back-end security via Vault-based encryption, granular eval permissions and web admin role, schema migrations, and CI/CD deployment workflows in collaboration with iOS and QA teams.
Technologies: Artificial Intelligence (AI), Python, Machine Learning, Natural Language Processing (NLP), TypeScript, SQL, Vercel, Supabase, Hugging Face, Architecture, Solution Architecture, AI Design, AI Programming, AI Chatbots, Conversational AI, Cloud Infrastructure, RAG Architecture, Startups, User Experience (UX), AI Agents, Agentic AI, API Integration, Visual Studio Code (VS Code), Chatbots, Codex, AI Architecture, MySQL, Data Privacy, Model Evaluation, AI Hallucinations Management, Automation, Automations, Workflow, Workflow Automation, Process Automation, Cursor AI, OpenAI, AI Model Training, Datasets, Minimum Viable Product (MVP), Software as a Service (SaaS), SaaS, Web Development, AI Engineering, Agentic RAG Systems, Retrieval-augmented Generation (RAG), Scalability, Technical Architecture, Data Protection, Claude Code, Recall, Transcription, Small Language Models (SLMs), Open-source LLMs, AI Modeling, GitHub, Machine Learning Infrastructure Engineer, Qwen, AI platform engineer, LLM Integration, Machine Learning Operations (MLOps), Supabase Auth, AI Agent Orchestration, AI Product Strategy, Large Language Model Operations (LLMOps), Communication, LLM Reasoning, Neural Networks, Documentation, Multimodal GenAI, Early-stage Startups, Analytics, Full-stack Development, Multi-tenant Architecture, Multi-tenant SaaS, Software Architecture, Auto-scaling Cloud Infrastructure, Data Visualization, Distributed Computing, Front-end, Full-stack, User Interface (UI), Large Language Models (LLMs), Integration, Cloud, AI Model Fine-tuning, Benchmarking, Memory Optimization, Performance Testing, Pipelines, SGLang
Full-stack AI Engineer
2025 - 2026
Crimson Education
- Led the development of the most advanced and thorough admission probability prediction and recommendation tool on the market for elite US colleges for use by 100 internal college strategists.
- Optimized the monetary cost of execution for existing AI pipelines for profile analysis by 89%, with speed increases of 270%.
- Architected scalable ML infrastructure serving Crimson Education’s global network of 2,400+ tutors, mentors, and strategists.
- Orchestrated the integration of AI prediction models with existing internal full-stack applications using RESTful APIs.
- Configured and deployed CI/CD pipelines for back-end systems on AWS App Runner via GitHub Actions and Docker.
- Collaborated across engineering and product teams to align technical requirements with business objectives.
- Implemented monitoring and logging systems via AWS CloudWatch for performance optimization across production environments.
Technologies: Python, PyTorch, FastAPI, Docker, CI/CD Pipelines, GitHub Actions, Amazon Web Services (AWS), PostgreSQL, GraphQL, React, TypeScript, Node.js, Git, Artificial Intelligence (AI), Machine Learning, REST APIs, Cloud Services, Large Language Models (LLMs), Containers, Kubernetes, APIs, OpenAI API, Prompt Engineering, Statistics, Anthropic, Claude, Generative Artificial Intelligence (GenAI), Data Modeling, Data Science, Normalization, Performance Optimization, Dimensionality Reduction, Pandas, JavaScript, AI Development, React Native, Human-in-the-loop (HITL), Applied Mathematics, Data Pipelines, Matplotlib, Data Engineering, Data Analysis, Claude API, Retrieval-augmented Generation (RAG), Architecture, Solution Architecture, Deep Learning, AI Design, AI Programming, AI Chatbots, Conversational AI, Cloud Infrastructure, Startups, User Experience (UX), AI Agents, Agentic AI, API Integration, RAG Systems, Visual Studio Code (VS Code), Chatbots, Codex, AI Architecture, MySQL, Data Privacy, Model Evaluation, AI Hallucinations Management, Automation, Automations, Workflow, Workflow Automation, n8n, Process Automation, Cursor AI, OpenAI, AI Model Training, Datasets, Minimum Viable Product (MVP), CRM, Customer Relationship Management (CRM), Software as a Service (SaaS), SaaS, Web Development, AI Engineering, Agentic RAG Systems, Scalability, Technical Architecture, Data Protection, Claude Code, Recall, Enterprise ready, Small Language Models (SLMs), Open-source LLMs, AI Modeling, GitHub, Machine Learning Infrastructure Engineer, Qwen, AI platform engineer, LLM Integration, Machine Learning Operations (MLOps), AI Agent Orchestration, AI Product Strategy, Enterprise Security, Large Language Model Operations (LLMOps), Communication, LLM Reasoning, Neural Networks, Documentation, Fine-tuning, Multimodal GenAI, Analytics, Full-stack Development, Software Architecture, Auto-scaling Cloud Infrastructure, Data Visualization, Distributed Computing, Front-end, Full-stack, User Interface (UI), CSV, Integration, Cloud, Benchmarking, Performance Testing, Pipelines
Software Project Advisor
2024 - 2026
Crimson Education
- Mentored 25 students one-on-one on long-term AI and software capstone projects with measurable social impact for Ivy League admissions.
- Designed and led courses teaching AI to students, covering topics such as Python, PyTorch, convolutional neural networks (CNN), transformers, and Docker containers.
- Advised across all project phases—ideation, development, deployment, and evaluation–for high-impact technology initiatives.
- Guided the development of projects in computational fluid dynamics analysis, medtech education, and robotics simulation.
- Supported implementation using Python, ML frameworks like scikit-learn and PyTorch, and full-stack web frameworks.
Technologies: Python, FastAPI, OpenCV, Arduino, PyTorch, Keras, TensorFlow, TypeScript, React, Next.js, Node.js, Docker, Google Cloud Platform (GCP), Railway, Scikit-learn, U-Net, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Supervised Learning, Transformers, Natural Language Toolkit (NLTK), Natural Language Processing (NLP), Prisma, Object Detection, Image Segmentation, Variational Autoencoders (VAEs), Git, Artificial Intelligence (AI), Machine Learning, REST APIs, Cloud Services, Large Language Models (LLMs), Containers, SQL, APIs, OpenAI API, Prompt Engineering, Vector Databases, Statistics, Image Recognition, Open-source LLMs, Open Source, Anthropic, Claude, Generative Artificial Intelligence (GenAI), Data Modeling, Data Science, Normalization, Performance Optimization, Forecasting, Pose Estimation, Video Analysis, Dimensionality Reduction, K-means Clustering, Linear Regression, Logistic Regression, Pandas, Random Forests, Time Series, JavaScript, AI Development, Teaching, Technical Instruction, React Native, Stable Diffusion, Image Generation, Text to Image, Text to Image AI, AI Art Generator, Applied Mathematics, Data Pipelines, Matplotlib, Sensor Data, Data Analysis, Video Processing, FFmpeg, Semantic Segmentation, GPU Computing, Graphics Processing Unit (GPU), Residual Neural Networks (ResNets), YOLOv5, You Only Look Once (YOLO), Claude API, Retrieval-augmented Generation (RAG), Architecture, Solution Architecture, Deep Learning, AI Design, AI Programming, AI Chatbots, Conversational AI, Cloud Infrastructure, RAG Architecture, Scalable Vector Databases, Startups, User Experience (UX), AI Agents, Agentic AI, API Integration, RAG Systems, Visual Studio Code (VS Code), Chatbots, AI Architecture, Optical Character Recognition (OCR), Model Evaluation, AI Hallucinations Management, Automation, Automations, Workflow, Workflow Automation, Process Automation, Cursor AI, OpenAI, AI Model Training, Datasets, Minimum Viable Product (MVP), Software as a Service (SaaS), SaaS, Web Development, AI Engineering, Scalability, Technical Architecture, Claude Code, Recall, Enterprise ready, AI Modeling, GitHub, Machine Learning Infrastructure Engineer, AI platform engineer, LLM Integration, Machine Learning Operations (MLOps), AI Product Strategy, Communication, Neural Networks, Documentation, Early-stage Startups, Analytics, Full-stack Development, Multi-tenant Architecture, Multi-tenant SaaS, Software Architecture, Auto-scaling Cloud Infrastructure, Data Visualization, Distributed Computing, Front-end, Image Processing, Full-stack, User Interface (UI), CSV, Integration, Cloud, AI Model Fine-tuning, Benchmarking, Memory Optimization, Performance Testing, Pipelines
Machine Learning Engineer
2025 - 2025
Evidentful Limited
- Leverage AI to develop tools for automated police witness interviews and witness statements to be presented in a court of law.
- Accelerated startup growth by helping secure Cambridgeshire Police for the first major pilot, securing $250,000 of Series A funding.
- Reduced interview transcription and response times by over 70% through the implementation of custom speech-to-text pipelines using Whisper, context optimizations, and interview state management.
- Collaborated with legal experts and police representatives to design and implement cutting-edge AI tools within legal compliance.
- Led the design and implementation of the automated interviews tech stack across numerous in-house supporting APIs.
- Secured the back-end environment of Evidentful’s systems to be ISO 27001 compliant.
- Deployed and maintained ML models via Azure Machine Learning and Azure AI Foundry, enabling scalable, secure, and cost-effective inference across production systems.
Technologies: Python, FastAPI, PyTorch, Azure, TypeScript, React, Tailwind CSS, Next.js, Prisma, GitHub Actions, CI/CD Pipelines, Docker, Azure Kubernetes Service (AKS), ChromaDB, Semantic Kernel (SK), Multi-agent Systems, Speech Synthesis, Whisper, ISO 27001, OAuth 2, JSON Web Tokens (JWT), DNS Servers, Virtual Private Cloud (VPC), Git, Artificial Intelligence (AI), Machine Learning, REST APIs, Cloud Services, Large Language Models (LLMs), Containers, Kubernetes, SQL, Hugging Face, APIs, OpenAI API, Prompt Engineering, Vector Databases, Open Source, Anthropic, Claude, Generative Artificial Intelligence (GenAI), Data Modeling, Data Science, Normalization, Performance Optimization, Dimensionality Reduction, JavaScript, AI Development, React Native, Human-in-the-loop (HITL), Applied Mathematics, Data Pipelines, Matplotlib, Data Engineering, Data Analysis, Video & Audio Processing, Claude API, Retrieval-augmented Generation (RAG), Architecture, Solution Architecture, Deep Learning, AI Design, AI Programming, AI Chatbots, Conversational AI, AI Voice Agents, Cloud Infrastructure, AI Avatars, Avatars, RAG Architecture, Scalable Vector Databases, Startups, User Experience (UX), AI Agents, Agentic AI, API Integration, RAG Systems, Visual Studio Code (VS Code), Chatbots, Supabase, AI Architecture, MySQL, Data Privacy, Model Evaluation, Speech-to-Text (STT), Text-to-Speech (TTS), AI Hallucinations Management, Automation, Automations, Workflow, Workflow Automation, Process Automation, OpenAI, AI Model Training, Datasets, Minimum Viable Product (MVP), Software as a Service (SaaS), SaaS, Web Development, AI Engineering, Agentic RAG Systems, Scalability, Technical Architecture, Data Protection, Recall, Transcription, Small Language Models (SLMs), AI Modeling, GitHub, Machine Learning Infrastructure Engineer, AI platform engineer, Ollama, LLM Integration, Machine Learning Operations (MLOps), Supabase Auth, AI Agent Orchestration, AI Product Strategy, Large Language Model Operations (LLMOps), Communication, LLM Reasoning, Neural Networks, Documentation, Fine-tuning, Multimodal GenAI, Early-stage Startups, Analytics, Full-stack Development, Multi-tenant Architecture, Multi-tenant SaaS, Software Architecture, Auto-scaling Cloud Infrastructure, Data Visualization, Distributed Computing, Front-end, Full-stack, User Interface (UI), Integration, Cloud, Benchmarking, Performance Testing, Pipelines
Machine Learning Engineer
2023 - 2025
Siglum Limited
- Developed sentence classification and extractive summarization models to yield the key information from text documents.
- Utilized sentence and word embedding models, such as BERT, RoBERTa, sentence transformers, and Vectara, for inserting conference papers and journal articles into an in-house vector database (ChromaDB) instance.
- Developed a Chroma-based vector database for semantic querying over a document corpus of 300 million academic papers.
- Architected REST APIs to use ChromaDB results to retrieve full documents from CockroachDB, then apply extractive summarization on relevant research for automated citation suggestion.
Technologies: Python, FastAPI, PyTorch, PostgreSQL, CockroachDB, ChromaDB, Vectara, BERT, Natural Language Processing (NLP), Natural Language Toolkit (NLTK), Google Cloud Platform (GCP), Elestio, Transformers, Docker, CI/CD Pipelines, GitHub Actions, Git, Artificial Intelligence (AI), Machine Learning, REST APIs, Cloud Services, Large Language Models (LLMs), Containers, Jupyter Notebook, SQL, Hugging Face, APIs, OpenAI API, Prompt Engineering, Vector Databases, Statistics, Open-source LLMs, Open Source, Anthropic, Claude, Generative Artificial Intelligence (GenAI), Data Modeling, Data Science, Normalization, Performance Optimization, Dimensionality Reduction, Linear Regression, Pandas, Ubuntu, AI Development, Applied Mathematics, Data Pipelines, Matplotlib, Data Engineering, Data Analysis, Claude API, Retrieval-augmented Generation (RAG), Architecture, Solution Architecture, Deep Learning, AI Design, AI Programming, Cloud Infrastructure, RAG Architecture, Scalable Vector Databases, Startups, AI Agents, Agentic AI, API Integration, RAG Systems, Visual Studio Code (VS Code), AI Architecture, Optical Character Recognition (OCR), MySQL, Data Privacy, Model Evaluation, AI Hallucinations Management, Automation, Automations, Workflow, Workflow Automation, Process Automation, OpenAI, AI Model Training, Datasets, Minimum Viable Product (MVP), Software as a Service (SaaS), SaaS, Web Development, AI Engineering, Agentic RAG Systems, Scalability, Technical Architecture, Data Protection, Recall, AI Modeling, GitHub, Machine Learning Infrastructure Engineer, AI platform engineer, LLM Integration, Machine Learning Operations (MLOps), AI Agent Orchestration, AI Product Strategy, Large Language Model Operations (LLMOps), Communication, LLM Reasoning, Neural Networks, Documentation, Early-stage Startups, Analytics, Full-stack Development, Multi-tenant Architecture, Multi-tenant SaaS, Software Architecture, Auto-scaling Cloud Infrastructure, Data Visualization, Distributed Computing, Front-end, Google Cloud, Computer Vision Algorithms, CSV, Integration, Cloud, AI Model Fine-tuning, Benchmarking, Memory Optimization, Performance Testing, Pipelines
AI Research Assistant
2022 - 2023
Victoria University of Wellington
- Developed a multi-model pipeline for the simultaneous detection, segmentation, and malignancy classification of breast lesions.
- Leveraged various single-shot detector, conditional U-Net segmenter, and CNN classifier networks.
- Conducted experimentation, hyperparameter tuning, and performance evaluation to achieve optimal performance across the full detection-segmentation-classification pipeline.
Technologies: Python, PyTorch, U-Net, Object Detection, Single-Shot Detectors, Image Segmentation, Image Classification, Medical Imaging, OpenCV, Convolutional Neural Networks (CNNs), Git, Artificial Intelligence (AI), Machine Learning, Jupyter Notebook, Hugging Face, Statistics, Image Recognition, Open Source, Data Modeling, Data Science, Normalization, Scientific Computing, Performance Optimization, Dimensionality Reduction, Pandas, Time Series, Ubuntu, AI Development, Applied Mathematics, Data Pipelines, Matplotlib, Data Analysis, Video & Audio Processing, Video Processing, Semantic Segmentation, GPU Computing, Scikit-image, Graphics Processing Unit (GPU), NVIDIA A100 Tensor Core GPU, Residual Neural Networks (ResNets), YOLOv5, You Only Look Once (YOLO), Architecture, Solution Architecture, Deep Learning, AI Design, AI Programming, Cloud Infrastructure, Startups, Visual Studio Code (VS Code), AI Architecture, Model Evaluation, Automation, Automations, Workflow, Workflow Automation, Process Automation, AI Model Training, Datasets, AI Engineering, Technical Architecture, Recall, AI Modeling, GitHub, Communication, Neural Networks, Documentation, Fine-tuning, Analytics, Software Architecture, Data Visualization, Distributed Computing, Image Processing, Computer Vision Algorithms, CSV, Cloud, AI Model Fine-tuning, Benchmarking, Memory Optimization, Performance Testing, Pipelines
Project History
AI-powered College Admissions Predictor & Recommendations Platform
Developed Crimson Education's flagship admissions probability prediction and recommendation system–the most advanced tool in the market for elite US college admissions strategy. The platform serves 100+ internal college strategists and supports a global network of 2,400+ tutors, mentors, and strategists with AI-driven insights for student placement.
I led the technical architecture and full-stack integration of ML prediction models with existing applications via RESTful APIs, while implementing scalable cloud infrastructure on AWS. I established automated CI/CD pipelines through GitHub Actions and Docker, complemented by comprehensive monitoring and logging systems via CloudWatch to ensure optimal performance across production environments. I also collaborated across engineering and product teams to align technical capabilities with strategic business objectives, delivering measurable improvements in both cost efficiency and system performance.
I led the technical architecture and full-stack integration of ML prediction models with existing applications via RESTful APIs, while implementing scalable cloud infrastructure on AWS. I established automated CI/CD pipelines through GitHub Actions and Docker, complemented by comprehensive monitoring and logging systems via CloudWatch to ensure optimal performance across production environments. I also collaborated across engineering and product teams to align technical capabilities with strategic business objectives, delivering measurable improvements in both cost efficiency and system performance.
GB10 Cluster Agentic Development Platform
I built a private MVP of a reproducible IaC project for GPU-backed local and cluster AI development, now being finalized for open-source release. The system enables users to work from their own machines using VS Code, SSH, and file-transfer clients, while the remote infrastructure handles heavier coding, inference, and agentic workloads. Focus areas include secure access, automated set up, containerized services, model and worker orchestration, and AI-assisted development workflows using tools such as Codex and Claude Code.
Cell Microscopy & Retinal Blood Vessel Medical Image Segmentation
https://github.com/gbymb4/Various-Biomedical-Image-Segmentation-MethodsA research project associated with my master's degree.
I developed various new neural network architectures and loss functions to solve cell microscopy and retinal blood vessel segmentation problems. I achieved state-of-the-art performance for pixel-level and topological-level metrics compared to baseline methods for both problems in this domain.
I developed various new neural network architectures and loss functions to solve cell microscopy and retinal blood vessel segmentation problems. I achieved state-of-the-art performance for pixel-level and topological-level metrics compared to baseline methods for both problems in this domain.
Datacenter Energy Optimization
A research project conducted while studying at Victoria University of Wellington.
I used evolutionary strategies like the reinforcement learning technique to train a hyperheuristic agent to dynamically allocate tasks and containers and virtual machines and boot physical machines in a simulated datacenter to reduce overall energy consumption. I achieved state-of-the-art performance on overall consumed energy compared to baseline methods in this problem domain.
I used evolutionary strategies like the reinforcement learning technique to train a hyperheuristic agent to dynamically allocate tasks and containers and virtual machines and boot physical machines in a simulated datacenter to reduce overall energy consumption. I achieved state-of-the-art performance on overall consumed energy compared to baseline methods in this problem domain.
Mammography Tumor Detection
A research project associated with my role as an AI research assistant at Victoria University of Wellington.
I used various single-shot detector, conditional U-Net segmenter, and CNN classifier networks, respectively, for the identification, segmentation, and malignancy classification of tumors within the human breast tissue. The full pipeline was capable of accurately cropping, masking, and determining the malignancy of lesions detected within mammogram images.
I used various single-shot detector, conditional U-Net segmenter, and CNN classifier networks, respectively, for the identification, segmentation, and malignancy classification of tumors within the human breast tissue. The full pipeline was capable of accurately cropping, masking, and determining the malignancy of lesions detected within mammogram images.
Education
2023 - 2024
Master's Degree in AI and Machine Learning
Victoria University of Wellington - Wellington, New Zealand
Skills
Libraries/APIs
PyTorch, React, Node.js, OpenCV, TensorFlow, Scikit-learn, Natural Language Toolkit (NLTK), REST APIs, OpenAI API, Pandas, Matplotlib, Claude API, Keras, FFmpeg, vLLM, PySpark, Hugging Face Transformers
Tools
Git, Claude, Scikit-image, You Only Look Once (YOLO), Codex, Claude Code, GitHub, Whisper, n8n, Prisma, Azure Kubernetes Service (AKS), Terraform, AI Prompts, Docker Compose
Languages
Python, TypeScript, SQL, JavaScript, GraphQL
Frameworks
Flask, Next.js, Tailwind CSS, JSON Web Tokens (JWT), React Native, Hadoop, OAuth 2
Paradigms
Automation, Distributed Computing, Model Context Protocol (MCP), Role-based Access Control (RBAC)
Platforms
Docker, Google Cloud Platform (GCP), Jupyter Notebook, Ubuntu, Visual Studio Code (VS Code), Ollama, Amazon Web Services (AWS), Azure, Kubernetes, Vercel, Arduino, Ubuntu Linux, Linux, ARM Linux
Storage
PostgreSQL, Data Pipelines, MySQL, Auto-scaling Cloud Infrastructure, Google Cloud, CockroachDB
Industry Expertise
Teaching, AI Art Generator, Enterprise Security
Other
FastAPI, BERT, CI/CD Pipelines, Computer Vision, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transformers, Supervised Learning, Reinforcement Learning, Image Segmentation, Slurm Workload Manager, Topological Loss Function Design, U-Net, GitHub Actions, Natural Language Processing (NLP), Image Classification, AI Research, Evolutionary Computation, Artificial Intelligence (AI), Machine Learning, Cloud Services, Large Language Models (LLMs), Containers, Hugging Face, APIs, Prompt Engineering, Vector Databases, Statistics, Image Recognition, Open-source LLMs, Open Source, Anthropic, Generative Artificial Intelligence (GenAI), Data Modeling, Data Science, Normalization, Scientific Computing, Performance Optimization, Decision Trees, Dimensionality Reduction, K-means Clustering, Linear Regression, Logistic Regression, Random Forests, Time Series, AI Development, Technical Instruction, Image Generation, Text to Image, Text to Image AI, Human-in-the-loop (HITL), Signal Processing, Applied Mathematics, Digital Signal Processing, Data Engineering, Data Analysis, Video & Audio Processing, Video Processing, Semantic Segmentation, GPU Computing, Graphics Processing Unit (GPU), NVIDIA A100 Tensor Core GPU, Residual Neural Networks (ResNets), YOLOv5, Retrieval-augmented Generation (RAG), Supabase, Architecture, Solution Architecture, Deep Learning, AI Design, AI Programming, AI Chatbots, Conversational AI, AI Voice Agents, Cloud Infrastructure, RAG Architecture, Scalable Vector Databases, Startups, User Experience (UX), AI Agents, Agentic AI, API Integration, RAG Systems, Chatbots, AI Architecture, Optical Character Recognition (OCR), Data Privacy, Model Evaluation, Speech-to-Text (STT), Text-to-Speech (TTS), AI Hallucinations Management, Automations, Workflow, Workflow Automation, Process Automation, Cursor AI, OpenAI, AI Model Training, Datasets, Minimum Viable Product (MVP), Software as a Service (SaaS), SaaS, Web Development, AI Engineering, Scalability, Technical Architecture, Data Protection, Recall, Transcription, Enterprise ready, Small Language Models (SLMs), AI Modeling, Machine Learning Infrastructure Engineer, Qwen, AI platform engineer, Cloud, LLM Integration, Large Language Model Operations (LLMOps), Agentic RAG Systems, Machine Learning Operations (MLOps), Supabase Auth, AI Agent Orchestration, AI Product Strategy, Communication, LLM Reasoning, Neural Networks, Documentation, Fine-tuning, Multimodal GenAI, Meta Llama, Early-stage Startups, Analytics, Full-stack Development, Multi-tenant Architecture, Multi-tenant SaaS, Software Architecture, Data Visualization, Front-end, Image Processing, Full-stack, User Interface (UI), Computer Vision Algorithms, CSV, Integration, AI Model Fine-tuning, Benchmarking, Memory Optimization, Performance Testing, Pipelines, ChromaDB, Object Detection, Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Adversarial Autoencoders, Railway, Semantic Kernel (SK), Multi-agent Systems, Speech Synthesis, Virtual Private Cloud (VPC), Vectara, Elestio, Single-Shot Detectors, Medical Imaging, Simulators, Time Series Analysis, Forecasting, Pose Estimation, Video Analysis, Gradient Boosting, Support Vector Machines (SVM), Stable Diffusion, Sensor Data, AI Avatars, Avatars, CRM, Customer Relationship Management (CRM), SGLang, ISO 27001, DNS Servers, Infrastructure, App Infrastructure, Server Infrastructure, Network Infrastructure, IT Infrastructure, Infrastructure Testing, Infrastructure as Code (IaC), Light LLMs, Multistage LLM Chains, RAG Pipelines, DNS, Scaling, Partitioning
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