Muhammad Azlan Yahaya

Muhammad Azlan Yahaya

Software Engineer

Software Engineer with deep expertise (8+ years) in LLMs, NLP, and Python-based AI application development. Skilled in building intelligent systems like NLP search platforms, Retrieval-Augmented Generation (RAG) systems, AI Agents and Chatbots. Experienced in developing end-to-end solutions using FastAPI/Flask, integrating vector databases, and deploying models on Cloud. Looking to apply cutting-edge AI/ML techniques in a dynamic and impactful engineering team.

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About

I'm an Software Engineer with over 8 years of experience building intelligent systems and scalable AI applications. With a Master's degree in Computer Science, I specialize in LLMs, NLP, and creating robust, cloud-native AI solutions that combine powerful backend systems with intuitive user interfaces. My expertise spans Python-based AI development, RAG systems, AI agents, and modern ML frameworks.

Throughout my career, I've worked on diverse AI/ML projects from NLP search platforms for medical databases to recommendation engines, RAG-based conversational AI, and deep learning model deployment. I'm comfortable working across the full AI/ML stack, delivering end-to-end solutions from model training and optimization to production deployment and monitoring. My approach focuses on applying cutting-edge AI/ML techniques while leveraging modern cloud infrastructure and DevOps practices.

8+
Years Experience
AI/ML
Primary Focus
Malaysia
Based In
Remote
Work Style

Experience

Senior AI/ML Engineer

The Right Contact

02/2023 – 10/2025 New York, United States

NLP Search Platform for Medical Database

  • Developed a natural language processing (NLP) search platform enabling clinicians and non-technical users to query a complex medical database using plain English
  • Engineered a transformer-based model to translate natural language queries into optimized SQL statements, significantly improving data accessibility
  • Integrated image-based search capabilities, allowing diagnostic image inputs (e.g., eye scans) to trigger relevant database queries
  • Applied machine learning algorithms to personalize and improve search accuracy over time by learning from user interactions
  • Architected the solution as scalable, cloud-native microservices deployed on AWS (Lambda, API Gateway, ECS, etc.)
  • Implemented advanced NLP features like synonym recognition and contextual query suggestions to enhance usability and query relevance

Python Developer

Enso Consulting

03/2021 – 12/2022 American Fork, United States
  • Developed a full-stack application enabling users to engage in conversational interactions with multiple documents. This application utilized a novel retrieval-augmented generation (RAG) pipeline, integrating LangChain and LlamaIndex
  • Implemented a state-of-the-art RAG pipeline incorporating both a traditional PostgreSQL database and a vector database, Chroma DB. This approach optimized data management and retrieval efficiency, enhancing the overall performance of the application
  • Built a scalable back end using FastAPI in Python, ensuring efficient API communication and data processing for the application. This back end provided the necessary infrastructure to handle user requests and manage the application's functionalities
  • Developed a user-friendly and visually appealing front end based on a Figma design using Svelte. This front end ensured an intuitive and engaging user experience, facilitating easy interaction with the application's features

Data Scientist/Data Engineer

Commune

01/2019 – 12/2020 San Mateo, United States
  • Designed and implemented a machine learning system that recognizes and extracts meaningful information from accounting documents using deep learning algorithms
  • Designed and implemented highly scalable, reliable, and distributed back-end services using Python, FastAPI, Celery, Redis, and MongoDB to process incoming data in real-time
  • Implemented the detection and classification of important objects on input documents, like QR codes, tables, headings, company logos, signatures, and many more

Python Developer

Aimesoft

08/2016 – 02/2019 Hanoi, Vietnam
  • Designed and trained deep learning models using PyTorch and TensorFlow for tasks such as classification, regression, and sequence modeling across NLP and computer vision domains
  • Built and evaluated traditional ML pipelines using scikit-learn, including feature engineering, model selection, and hyperparameter tuning for structured data problems
  • Developed end-to-end model training workflows with support for custom loss functions, callbacks, and early stopping, ensuring optimal performance and generalization
  • Deployed trained models to production environments using ONNX, TorchScript, or TensorFlow Serving, and integrated them into cloud-native inference pipelines
  • Specialized in model optimization, data preprocessing, and scalable training/inference strategies for real-time and batch prediction use cases

Education

Master of Computer Science

Nilai University

2015 – 2016 Negeri Sembilan, Malaysia

Bachelor of Computer Science

Nilai University

2012 – 2015 Negeri Sembilan, Malaysia

Skills

Core Focus

Large Language Models (LLMs)
Natural Language Processing (NLP)
RAG Systems & AI Agents
Deep Learning & Model Training
Python AI Development
Cloud AI/ML Infrastructure

Programming Languages

Python C++ Java TypeScript JavaScript

Web Frameworks

FastAPI Flask Node.js Express.js React.js Next.js

ML & AI Frameworks

PyTorch TensorFlow Scikit-learn Hugging Face Transformers LangChain LangGraph LlamaIndex AutoGen CrewAI AWS Bedrock AWS SageMaker Azure AI Studio Azure AI Search GCP Vertex AI Google AI Studio

NLP & LLMs

OpenAI Azure Anthropic Gemini Groq IBM watsonx Retrieval-Augmented-Generation (RAG) Named Entity Recognition (NER) Tokenization Stemming and Lemmatization Sentiment Analysis Topic Modeling Text Summarization Part-of-Speech (POS) Tagging

Databases & Vector Database

MySQL PostgreSQL MongoDB Redis Pinecone Weaviate ChromaDB FAISS PGVector Qdrant Amazon OpenSearch Service Azure AI Search Azure Cosmos DB

DevOps & Cloud

AWS Azure GCP Kubernetes Docker CI/CD Git

Tools & APIs

RESTful APIs GraphQL LangServe OAuth JWT WebSocket Mapbox GL

System Design

Microservices Secure Authentication Load Balancers ASGI

Selected Projects

NLP Search Platform for Medical Database

Senior AI/ML Engineer

Developed a natural language processing (NLP) search platform enabling clinicians to query complex medical databases using plain English. Engineered transformer-based model to translate NL queries into optimized SQL, with image-based search capabilities and ML-powered personalization. Deployed on scalable AWS microservices.

NLP Transformers Python FastAPI Machine Learning AWS Microservices

Multi-Document Conversational RAG Application

Python Developer

Developed a full-stack application enabling users to engage in conversational interactions with multiple documents utilizing a novel RAG pipeline integrating LangChain and LlamaIndex. Implemented state-of-the-art RAG pipeline with PostgreSQL and ChromaDB, built scalable FastAPI backend, and developed user-friendly Svelte frontend based on Figma designs.

RAG LangChain LlamaIndex FastAPI PostgreSQL ChromaDB Svelte

ML-Powered Document Information Extraction

Data Scientist/Data Engineer

Designed and implemented a machine learning system that recognizes and extracts meaningful information from accounting documents using deep learning algorithms. Built highly scalable distributed backend services using Python, FastAPI, Celery, Redis, and MongoDB for real-time processing. Implemented detection and classification of important objects like QR codes, tables, headings, logos, and signatures.

Deep Learning Document Processing FastAPI Celery Redis MongoDB Object Detection

Deep Learning Model Training & Deployment

Python Developer

Designed and trained deep learning models using PyTorch and TensorFlow for classification, regression, and sequence modeling across NLP and computer vision domains. Deployed models to production using ONNX, TorchScript, and TensorFlow Serving, integrated into cloud-native inference pipelines.

PyTorch TensorFlow Deep Learning Model Deployment ONNX Cloud Inference

ML Pipelines & Feature Engineering

Python Developer

Built and evaluated traditional ML pipelines using scikit-learn, including feature engineering, model selection, and hyperparameter tuning for structured data problems. Developed end-to-end model training workflows with custom loss functions, callbacks, and early stopping for optimal performance and generalization.

Scikit-learn Feature Engineering Model Training Hyperparameter Tuning Python

Model Optimization & Production Deployment

Python Developer

Specialized in model optimization, data preprocessing, and scalable training/inference strategies for real-time and batch prediction use cases. Deployed models to production environments and integrated them into cloud-native inference pipelines with focus on performance and reliability.

Model Optimization Production ML Real-time Inference Cloud Deployment Performance Tuning

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Contact

I'm always open to discussing new projects, creative ideas, or opportunities to be part of your vision. Feel free to reach out!

WhatsApp

+1 227 277 6661