Designing, deploying, and scaling production-grade AI systems. Specializing in multi-agent architectures, RAG pipelines, and LLM optimization that turn complex business problems into reliable products.
Senior AI/ML Engineer specializing in production-grade AI systems, multi-agent architectures, Retrieval-Augmented Generation (RAG), and LLM optimization. I've designed and deployed end-to-end AI systems that automate complex workflows, reduce manual analysis by up to 85%, and cut LLM inference costs by as much as 60% through intelligent orchestration, retrieval, and optimization. My open-source fine-tuned language model has surpassed 12,000 downloads on Hugging Face while achieving 97.8% task accuracy, reflecting my focus on building efficient models that solve real-world problems. From distributed multi-agent pipelines and scalable FastAPI backends to production RAG systems and LLM evaluation workflows, I build AI products that move beyond demos into reliable, maintainable systems used by real teams. If you're building serious AI infrastructure - I'd love to connect.
• Led deployment of hierarchical multi-agent AI for automated contract intelligence.
• Built critic-refinement evaluation loops, significantly reducing hallucinations.
• Integrated Google Gemini with robust cost-control mechanisms.
• Designed and deployed end-to-end AI systems that reduced manual analysis by up to 85%.
• Optimized LLM inference costs by 60% through intelligent orchestration and retrieval.
• Built production RAG pipelines with token-aware chunking and vector search.
• Bridged technical AI capabilities with product strategy and roadmap execution.
• Coordinated cross-functional teams delivering AI-powered features to production.
• Managed experimentation loops and performance benchmarking for ML models.
Problem: Smart home tasks required cloud-heavy, high-latency models.
Solution: Fine-tuned a local language model optimized for offline deployment (228MB footprint) using PyTorch and LoRA.
Impact: Surpassed 12,000 downloads on Hugging Face with 97.8% task accuracy.
Problem: Smart home assistants depend on cloud APIs, creating latency and privacy concerns.
Solution: Built a fully local assistant powered by a custom-trained LFM2.5 model with zero cloud dependencies.
Impact: Runs entirely on consumer hardware with sub-second response times.
Problem: Manual contract analysis was slow, error-prone, and hallucination-heavy.
Solution: Designed hierarchical multi-agent orchestration with critic-refinement evaluation loops using Google Gemini.
Impact: Reduced hallucinations and cut manual analysis time by up to 85%.
Problem: Businesses need intelligent conversational agents with domain-specific knowledge.
Solution: Built a RAG-enhanced chatbot using Gemini API with token-aware chunking and Pinecone vector search.
Impact: Reduced LLM inference costs by 60% while maintaining accuracy.
Problem: Training autonomous driving agents requires safe, repeatable simulation environments.
Solution: Built a Deep-Reinforcement Learning agent using Computer Vision and CARLA simulator.
Impact: Agent learns navigation policies through reward optimization in realistic 3D environments.
Problem: Digitizing handwritten text at scale with high accuracy.
Solution: CNN extracts feature maps from handwritten images, passed through LSTM blocks for sequence prediction.
Impact: End-to-end trainable pipeline for cursive and printed handwriting recognition.
Problem: Low-resolution images lack detail for professional and analytical use cases.
Solution: Pre-trained SRGAN generates photo-realistic super-resolution images from low-res inputs.
Impact: Produces visually convincing upscaling for medical imaging and satellite data.
A deep dive into how I built a personal AI assistant using Google's Gemini API. We explore prompt engineering and context handling in Python Flask.
How Deep Reinforcement Learning can be applied to autonomous driving simulations. Challenges faced in training agents and optimizing reward functions.
Tips on structuring a developer portfolio. Why combining AI projects with solid Full-Stack skills makes you a stronger candidate in the Nigerian tech market.