Bernard Okwampah

• Designed and implemented the complete technical architecture for a hybrid AI and blockchain-powered prediction market platform from the ground up.

• Defined the platform's Web3 infrastructure, evaluating blockchain networks (Ethereum, Polygon) and selecting the most suitable for scalability, security, and smart contract deployment.

• Led the development of the web application (front-end and back-end), using Python and modern frameworks to deliver the MVP within three months.

• Built a scalable AI aggregation engine that integrated multiple large language models (LLMs) and APIs to improve prediction accuracy by over 30%.

• Collaborated with the founding team to align the technical roadmap with product milestones and investor deliverables.

• Created comprehensive architecture diagrams, documentation, and sprint plans to support development, onboarding, and future team scaling.

• Managed the end-to-end AI infrastructure for an adaptive learning platform, ensuring model scalability and performance under growing user load.

• Collaborated with the engineering team to integrate machine learning APIs into the core product, improving personalization and student engagement.

• Monitored, maintained, and optimized deployed ML models for accuracy, latency, and data pipeline efficiency.

• Contributed to system architecture decisions, balancing performance needs with budget constraints on cloud infrastructure.

• Supported the development of internal tools for data labeling, model evaluation, and version control.

• Documented ML workflows, deployment processes, and technical protocols for team knowledge sharing and onboarding.

• Led the design and development of Neuron, an AI-powered personal growth assistant built to help users break through stagnation and develop a growth mindset. The tool provided goal setting, personalized research, productivity tracking, and intelligent planning features using LLMs.

• Built CVme_AI, a gamified AI job search tool that allowed users to swipe through job opportunities Tinder-style, and auto-apply based on user preferences and optimized CV matches.

• Architected and implemented backend systems for both products using Python and AI APIs, including OpenAI and HuggingFace models.

• Integrated user behavior analytics and feedback systems to refine recommendations and enhance engagement over time.

• Led a small distributed team, overseeing technical tasks, product strategy, and agile planning.

• Created detailed technical documentation and product roadmaps for scaling both tools into beta-ready MVPs.