Summary
Muhammed Y. is a talented Python Developer and Data Scientist with expertise in building high-performance backend systems, automation workflows, and AI-driven applications. Muhammed is proficient in Python, Cython, JavaScript, and SQL, and has hands-on experience with frameworks like FastAPI, Sanic, Flask, and Django. He is skilled in database management, working with PostgreSQL, MySQL, and MongoDB, and applies test-driven development (TDD) and network programming principles to backend development. His data science expertise includes NumPy, Pandas, Matplotlib, Scikit-learn, and deep learning neural networks, allowing him to extract insights and optimize systems. At Mango Zest Labs, he has contributed to scalable AI API deployments, containerized machine learning models using Docker, and secured authentication system. Previously, at Fagmaz Hydra Ventures, he developed automated inventory management systems, FastAPI-powered admin endpoints, and machine learning solutions for business intelligence. With a strong background in containerization, blockchain research, and cybersecurity, Muhammed is a highly adaptable engineer capable of delivering efficient, secure, and scalable solutions. His passion for innovation and problem-solving makes him a valuable asset to any tech-driven organization.
Meet Muhammed Y.
Assessment Scores
Perfect score on H Distance Question, Difficulty Easy
Great score on Two Algorithm Questions, Difficulty Medium
Good score on String Scramble Question, Difficulty Medium
Perfect score on Python S3 Get File
Perfect score on Python S3 Get Contents
Experience
Mango Zest Labs
Backend Developer
Part-Time
Jun 2024 — Sep 2025
Develop and deploy backend APIs for interacting with Generative AI (GenAI) models
Build and manage secure authentication layers for AI-driven applications
Deploy and maintain containerized HashiCorp Vault instances for secure secrets management
Architected and led development of a production‑grade Conversational AI engine used to power products such as virtual call centers and automated customer interaction platforms
Designed the system as a layered architecture, comprising: o SIP ingress layer handling inbound and outbound calls o A dedicated call orchestrator that bridges SIP sessions to the engine’s WebSocket endpoints o Speech‑to‑Text (STT) ingestion for real‑time audio transcription o A RAG‑based context engine for domain‑aware knowledge retrieval o A reasoning and orchestration layer for intent resolution, workflow execution, and tool invocation o Text‑to‑Speech (TTS) voice synthesis for natural, low‑latency responses
Built scalable backend services to coordinate real‑time audio streaming, context enrichment, and response generation while maintaining reliability under high‑concurrency call sessions.
Developed a stablecoin‑based payment engine enabling low‑fee, near‑instant settlement for customer checkouts.
Researched and delivered a Web3 proof‑of‑concept for business registration using smart contracts and asset tokenization on the XRP Ledger.
Technologies:
Python
Docker
FastAPI
OAuth 2.0
XRP ledger
openai
whisper
transformers
LLM
Agentic AI
AI
Fagmaz Hydra Ventures
Python Developer
Full-Time
Jan 2021 — May 2024
Created scripts for data processing, task automation, and workflow optimization
Implemented internal file management systems for secure file sharing
Improved operational efficiency by ~38% through automation, better data visibility, and workflow optimization.
Designed and maintained an internal inventory management platform with real‑time analytics, admin endpoints, and automated weekly reporting.
Performed agricultural and market data analysis to support segmentation, forecasting, and weather‑driven decision making.
Technologies:
Python
PostgreSQL
PyQt
FASTAPI
Numpy
Pandas
Matplotlib
scikit-learn
Data Analytics
flask
RESTApi
Data Quotient Limited
Backend Engineer
Part-Time
Jan 2025 — Jan 2026
Led backend architecture for production-grade Data-as-a-Service (DaaS) and AI platform, achieving sub-100ms API response times under load
Designed specialized DaaS sub-product from scratch, handling 10,000+ concurrent requests with opGmized query planning and caching
Implemented NDPR-compliant security controls, authentication flows, and tenant isolation for enterprise clients
Collaborated with data science and product teams to expose scalable, analytics-ready APIs powering Al-driven insights and ML workflows
Technologies:
Python
FastAPI
OAuth 2.0
MongoDB
PostgreSQL
Amazon Document DB
AWS S3
Cloudinary
Kotlin
Ktor
API integration
Data scraping
Data Pipelines
Projects
• Tech stack: Python, FastAPI, redis, OAUTH 2.0, postgresql, asyncpg, bcrypt, bloom filters • Designed production-grade, multi-tenant IAM platform providing secure authentication, authorization, and identity lifecycle management for SaaS applications • Implemented OAuth/OlDC-compliant authentication flows supporting PKCE, authorization codes, and refresh-token rotation • Built fine-grained authorization with RBAC, FGA, and ABAC-style policies, enforcing strict per-tenant isolation at application and database layers • Designed Redis-backed deterministic token revocation system using Bloom filters for 0(1) revocation checks with constant-time validation, avoiding per-request database calls or introspection endpoints while maintaining strong security guarantees where false positives result in forced re-authentication but never privilege escalation • Enforced strong tenant isolation using PostgreSQL Row-Level Security (RLS), enabling safe multi-tenant operation across shared infrastructure
(Data quotient) Leveler — Multi-Tenant DaaS & Al Platform
• Tech Stack: Python, FastA PI, PostgreSQL, Redis, AWS S3, RAG, Docker • Architected multi-tenant Data-as-a-Service platform aggregating Nigerian and African datasets with market-place for dataset and ML/AI model monetization • Implemented RAG-powered business intelligence with real-time database connectivity for natural-language querying and insight generation • Built configurable, event-driven agentic workflows supporting data enrichment, compliance checks, analytics pipelines, and custom business logic with strict per-tenant isolation
Conversational AI Engine (Mango Zest Labs)
• Tech stack: Python, FastAPI, RabbitMQ, Redis, Open-weight models (faster-whisper, piper-tts, chatterbox-ai, Mistral, Vicuna, Llama 3.2+), RAG, Refrag, postgresql, pgvector • Architected production-grade Conversational AI engine powering virtual call centers and automated customer interaction platforms • Designed layered architecture comprising: SIP ingress layer for call handling, call orchestrator bridging SIP sessions to engine's WebSocket endpoints, STT ingestion for real-time transcription, RAG-based context engine, reasoning/orchestration layer for intent resolution, and TTS voice synthesis • Built scalable backend services coordinating real-time audio streaming, context enrichment, and response generation under high-concurrency call sessions
• Tech Stack: Python, Streamlit, Prophet, XGBoost, Random Forest, Pandas • Built interactive dashboards integrating Prophet, XGBoost, and Random Forest models for time-series forecasting • Delivered real-time demand and inventory forecasts, enabling proactive inventory management and reduced stock-outs by 25%
Domain-specific-search-engine
• Tech Stack: Python, Streamlit, OpenAI API, FAISS • Developed Retrieval-Augmented Generation application enabling natural-language querying over domain-specific datasets • Delivered precise, context-aware answers by combining semantic retrieval with LLM-based reasoning
Education
Bachelor of Science
2022 - 2026
University of Osun State
Nigeria
