Oluwatosin – PyTorch, AWS, Python

Developed a Generative AI–powered bookkeeping agent that automates the extraction of accounting entries, assigns GL and tax codes, and performs bank reconciliation. The system processes around 10 invoices every 3 minutes and supports multiple communication channels, including WhatsApp, email, and Slack. Designed for accounting firms, SMEs, and startups, the solution delivers high efficiency—up to 10× faster and 4× more cost-effective than traditional bookkeeping workflows.

• Led the development of an AI-powered accounting agentic system and designed end-to-end AI/ML architecture;
• Built scalable document-processing infrastructure handling tens of thousands of invoices monthly, including a custom VLM that outperformed traditional OCR;
• Developed a multi-agent system for accounting tasks using open-source foundation models and implemented RAG/GraphRAG pipelines for accurate extraction and reasoning;
• Optimized and scaled LLM/VLM inference using Kubernetes, GPU multi-LoRA adapters, quantization, flash attention, and CUDA-optimized engines (vLLM, LMDeploy);
• Designed ETL and automation workflows with Airflow for continual fine-tuning and model improvement;
• Created a custom orchestration layer (replacing LangChain and others) and built an in-house vector database for semantic search;
• Delivered a production transactional accounting agent using React, Python, FastAPI, and Slack SDK, deployed via AWS ECS, Docker, and Terraform;
• Established MLOps practices, CI/CD pipelines, observability (Grafana, Elasticsearch), and grew the engineering and AI teams from the ground up.

AI-powered contact center platform that analyzes conversations across digital and voice channels, delivers real-time coaching to advisors, and provides leaders with actionable insights into 100% of customer demand and performance metrics.

• Built and deployed transcription (Speech2Text wav2vec-large-xlsr model) technology for optimizing call center productivity for client queries;
• Used SQL best practices to design PostgreSQL back-end databases for insights;
• Streamlined ETL processes, reducing errors due to manual data entry;
• Built a multi-tenancy speech text classifier model using the Transformer DistilRoBerta model for client call center data, which were poorly mixed up during database setup for multiple customers' environments in Client Server;
• Built and deployed a continuous customer prioritization algorithm for managing customer churn rates through continuous training and inference using Ludwig and Ray clusters for a state-of-the-art MLOps Pipeline;
• Performed QA analysis of data-centric frontend product features, such as metrics, filters, and weights of call center agent performance with customers by querying MongoDB and transformation using pandas and numpy.

AI-driven platform for assessing news article credibility and fact-checking. The system uses transformer models to detect article stance and credibility, incorporates an evidence retrieval pipeline for verification, and includes automated CI/CD workflows for continuous model updates and deployment.

• Developed and deployed a news article credibility algorithm using DistilRoBERTa, applied commercially for fact-checking major events such as the 2020 US presidential debate and monitoring misinformation during Euro 2020;
• Built a stance detection model to identify agreement and disagreement, mitigating coordinated attacks and manipulative threats;
• Created an evidence retrieval system combining BM25 ranking and semantic search with embeddings, powering the fact-checking pipeline by sourcing claims from media and social platforms;
• Implemented CI/CD pipelines for continuous machine learning deployment on GCP;
• Developed an aspect-based topic modeling system to extract and generate topic terms from social media content, enhancing data analysis and monitoring.

AI-powered assistive technology solutions designed to support inclusive education and enhance independence for individuals with visual impairments or reading disabilities, such as dyslexia and ADHD. The core product includes a mobile app and smart reading glasses to facilitate accessible reading and learning experiences.

• Built a rule-based Clinical Decision Support system to detect early signs of blindness;
• Developed multisensory deep learning algorithms for embedded smart reading glasses;
• Implemented Optical Character Recognition (OCR) for real-time text capture;
• Created Text-to-Speech models using RNNs to provide audio output for visually impaired users;
• Integrated hardware and software components to optimize the performance of smart reading devices;
• Conducted testing and fine-tuning to ensure accessibility, accuracy, and responsiveness of the system.