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AI Engineer with 5+ years in Machine Learning & AI
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AI & Data Science professional with expertise in large language models, machine learning, data analytics, and software reverse engineering. Experienced in building data pipelines, deploying AI models, and developing data visualisation applications. Currently completing an MSc in Artificial Intelligence at De Montfort University, with hands-on research experience in LLM integration, computer vision, real-time AI systems, simulation-based game environments, and automated evaluation pipelines.
De Montfort University
MSc · Artificial Intelligence
August 1, 2025 – June 30, 2026
University of Port Harcourt
BSc · Mathematics & Statistics
September 1, 2014 – January 1, 2019
Alice (Formerly Active Fence)
AI/ML Contractor
May 1, 2025 – Present
Ramat Gan, Tel Aviv District, Israel
Dojah(YCW22)
Data Analyst / Software Developer
January 1, 2021 – December 1, 2024
San Francisco, California, United States
Multi-Provider Generative Media Pipeline (T2I / T2V / I2V)
May 1, 2025 – June 30, 2026
Designed and implemented a production-grade generative media orchestration pipeline supporting text-to-image, text-to-video, and image-to-video workflows across multiple providers. Integrated OpenAI (GPT Image), Stable Diffusion 3.5 (Stability AI), PixVerse (v5 / v5.5), WAN Video, and Seedance, with dynamic provider routing and model version control. Built a Google Sheets-driven execution layer, enabling large-scale prompt management, ID-based batching, and non-technical control over generation runs. Implemented asynchronous job handling (submission, polling, failure states, retries) for video generation APIs, ensuring reliable end-to-end execution. Automated asset delivery and lifecycle management via Google Drive, enforcing client-defined naming conventions and structured output tracking. Added robust idempotency and safety controls (prompt-ID ranges, early-exit logic, output existence checks) to support safe restarts and prevent duplicate generation. Secured credentials and execution using Databricks secrets, environment isolation, and provider-specific authentication. Optimised execution for Databricks notebook and job environments, handling rate limits, partial failures, and delayed external writes.
Malicious Ads Detector
May 1, 2025 – June 30, 2026
Developed an AI pipeline to detect and classify harmful or deceptive online advertisements. Built a scalable preprocessing workflow (text + image signals), leveraging transformers and rule-based filters for robust detection. Reduced false positives by optimising model thresholds and incorporating human-in-the-loop validation.
Docling + OpenAI Document Intelligence Pipeline
May 1, 2025 – June 30, 2026
Engineered a document chunking and enrichment pipeline combining Docling with OpenAI models for large-scale document processing. Automated text chunking, semantic enrichment, and structured output (CSV/JSON), streamlining downstream analytics. Increased document processing throughput and reduced manual review time significantly
Taxonomy-Aware Trend Classification with GPT-4
May 1, 2025 – June 30, 2026
Designed and deployed an LLM-powered classification system to identify and categorise misinformation trends across digital platforms. Applied taxonomy-aware NLP techniques to cluster content, improving detection accuracy and explainability. Built a Retrieval-Augmented Generation (RAG) system to ground GPT-4 responses in verified sources, enhancing reliability and factual consistency in trend reports. Delivered structured insights that enhanced moderation and monitoring workflows.
AI Immigration Assistant: RAG vs Fine-Tuning Comparison
January 1, 2025 – May 1, 2026
Developed an AI Immigration Assistant, comparing RAG and fine-tuning approaches.
SLAM navigation system using ROS1 and Waffle
January 1, 2025 – June 30, 2026
Built a SLAM navigation system using ROS1 and Waffle as a volunteer for a summer robotics research programme, gaining hands-on experience in how AI solutions integrate with real-world robotics hardware, and developing a practical understanding of ROS1 versus ROS2 trade-offs, sensor integration, and the value of leveraging existing frameworks over reinventing solutions from scratch.
Towards a Generative Narrative Layer for Driver Training Simulators
January 1, 2025 – May 1, 2026
Dissertation project focusing on generative narrative layers for driver training simulators.
SLAM navigation system using ROS1
January 1, 2025 – May 1, 2026
Developed a SLAM navigation system using ROS1, integrating simultaneous localisation and mapping for autonomous robot navigation.
Cultural Fit Analysis
The candidate's diverse project portfolio, ranging from generative media pipelines to misinformation detection and robotics, indicates adaptability and a broad interest in AI applications. Their experience in both professional and academic settings, including a volunteer research program, suggests a continuous learning mindset and a willingness to engage with new challenges. The blend of AI/ML contractor and data analyst roles shows versatility, which is beneficial for dynamic team environments. The focus on practical, deployable solutions aligns well with a results-oriented culture.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving skills through their academic and professional projects, particularly in handling complex asynchronous operations and optimizing AI pipelines. Their experience mentoring analysts suggests good communication and leadership potential. The detailed project descriptions indicate a structured approach to development and an understanding of operational considerations like idempotency and error handling. The volunteer experience in robotics also highlights a proactive and collaborative attitude.