Samuel Lemikan — Software Engineer

(AI Engineering & Web3 Systems)

"Building the bridge between deterministic logic and probabilistic reasoning."

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👨‍💻 About Me

I am a software engineer specializing in the convergence of artificial intelligence, distributed systems, and blockchain architecture. My work focuses on building production-ready systems that are measurable, repeatable, and resilient.

I don't just write code; I design architectures that solve complex business problems. Whether it's optimizing inference pipelines for Large Language Models (LLMs) or writing gas-optimized circuits for Zero-Knowledge proofs, my goal is to create software that is both intelligent and trustless.

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🚀 Domain Expertise

My engineering focus is split into two high-impact verticals:

🤖 Applied AI Engineering Agentic Workflows: Designing multi-agent systems where AI models can execute autonomous tasks via function calling. RAG Architectures: Building high-fidelity Retrieval-Augmented Generation systems for business intelligence. Predictive Analytics: deploying models that turn raw data into actionable foresight.

🌐 Web3 Systems Engineering ZK Circuit Design: Writing privacy-preserving logic using Circom and SnarkJS. Smart Contract Security: Developing EVM-compatible contracts with a focus on reentrancy protection and gas optimization. Protocol Integration: Bridging off-chain data with on-chain events using Oracles and Indexers.

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🛠️ Technical Arsenal

Languages	AI & Data	Blockchain	Infrastructure

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📂 Featured Projects

🔐 ZK Proof Integration Privacy-Preserving Computation A robust validation framework leveraging Zero-Knowledge proofs. This allows for verifiable computation where the prover can demonstrate knowledge of a secret without revealing the secret itself. Circuit Logic: Custom .circom templates for constraint generation. Proof Generation: Client-side proof generation using SnarkJS. Verifier: On-chain Solidity verifier contracts.

🤖 MemoMate AI Long-Term Memory Architecture An intelligent memory layer for LLMs that solves the context window limitation. MemoMate uses vector indexing to store and retrieve user history, enabling AI agents to "remember" across sessions. Vector Store: Semantic search implementation for relevant context retrieval. Pipeline: Optimized LangChain flows for query decomposition. API: Fast API endpoints for seamless frontend integration.

💼 Business Manager SaaS Enterprise Resource Planning A full-stack SaaS platform designed for workflow automation. It features real-time analytics, role-based access control (RBAC), and an extensible plugin architecture. Architecture: Serverless architecture for infinite scalability. Database: Relational data modeling with Prisma ORM.

🌐 High-Scale Backend Social Media Infrastructure A backend system engineered for high concurrency. Capable of handling thousands of requests per second with data integrity guarantees. Caching: Aggressive Redis caching strategies. Queues: Event-driven architecture using message brokers.

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🧠 Engineering Principles

1. Reliability Over Novelty: New tech is only useful when grounded in stable engineering. I build systems that endure predictable behavior and maintainable complexity.
2. Intelligence Where It Matters: I integrate intelligence deeply into workflows and data infrastructure to create practical, measurable value—not just "AI wrappers."
3. Verifiability & Transparency: I design with determinism in mind: traceable execution, cryptographic assurance, and decentralized decision paths.

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