Summary
Muhammad M, A is an AI/ML Engineer with over two years of experience building and deploying production-ready intelligent systems across GenAI, NLP, computer vision, and data engineering. He specializes in designing end-to-end LLM pipelines, leveraging tools like LangChain and LlamaIndex, and optimizing model performance through techniques such as quantization and efficient serving strategies. He has strong backend engineering expertise, developing scalable RESTful APIs and microservices using FastAPI and Django, and integrating machine learning models into real-world applications. His experience extends to building robust NLP pipelines with Hugging Face Transformers, implementing RAG systems, and working with embeddings, FAISS, and vector-based retrieval. On the data engineering side, he designs scalable ML workflows using Apache Spark and Delta Lake on Databricks, while managing full MLOps lifecycles with Docker, CI/CD pipelines, and MLflow for experiment tracking and deployment. Proficient in Python, SQL, and Bash, he also works with modern databases such as MongoDB and PostgreSQL, and develops AI-powered applications involving OCR, ASR, and computer vision models like YOLOv8. Muhammad combines strong system design, model optimization, and deployment skills to deliver scalable, high-performance AI solutions that drive measurable impact in production environments.
Meet Muhammad Moazzam A.
Assessment Scores
Great score on Count Subarrays with Sum Equal to K, Difficulty Easy
Great score on Classification Model Inaccuracy, Difficulty Easy
Great score on Diagnose and Fix RAG Pipeline Retrieval Issues, Difficulty Medium
Great score on Word Search in Grid, Difficulty Medium
Great score on Validate Parentheses in a String, Difficulty Hard
Great score on Reverse Digits of a 32-bit Integer, Difficulty Medium
Experience
Devsinc
AI/ML
Software Engineer (AI/ML)
Full-Time
Jul 2025 — Mar 2026
Engineered and deployed production-grade LLM/GenAI pipelines, reducing inference latency by over 30% through model quantization and optimized serving strategies.
Managed end-to-end MLOps lifecycle including model versioning, containerization with Docker, and CI/CD integration to streamline deployment across environments.
Collaborated with cross-functional teams to translate business requirements into scalable AI solutions, ensuring reliability and performance in production.
Technologies:
Python
Ai agent
LLM pipelines
RAG
RetroX Interactive
AI/ML
AI/ML Engineer
Full-Time
Jun 2023 — Jun 2025
Developed and maintained NLP-powered backend services using FastAPI, processing and analyzing text data to support internal team workflows and decision-making.
Built and fine-tuned text processing pipelines leveraging Hugging Face Transformers, improving entity extraction and classification accuracy across multiple internal tools.
Designed RESTful APIs integrating ML models into production systems, enabling seamless consumption of AI capabilities by internal stakeholders.
Projects
Police Call Transcription & Analysis System
Built an end-to-end audio transcription pipeline for police emergency calls, converting speech to text using ASR models and achieving high accuracy across diverse caller accents and environments. Developed an AI-powered analysis layer that evaluates call sentiment, detects escalating or critical situations, and flags high-risk interactions in real time. Implemented an automated agent performance evaluation module scoring officers on call handling quality, protocol adherence, and communication effectiveness. Exposed all functionality through a RESTful API built with FastAPI, enabling seamless integration with law enforcement dispatch and monitoring systems.
Scalable ML Pipeline with Databricks & Delta Lake
Designed and implemented an end-to-end ML pipeline on Databricks using Apache Spark for large-scale data processing and feature engineering. Leveraged Delta Lake for ACID-compliant data storage, enabling reliable versioning, time travel, and incremental data updates across the pipeline. Orchestrated model training, validation, and deployment workflows using Databricks MLflow, tracking experiments and managing the full model lifecycle.
Asset Management & Insurance Analysis App
Built a Django-based web application that extracts asset and policyholder information from insurance documents using OCR (Tesseract) and open-source vision models. Implemented an intelligent analysis layer that evaluates coverage adequacy, identifies coverage gaps, and determines whether individuals require additional or new insurance policies. Integrated an open-source LLM to generate natural-language summaries and recommendations based on extracted policy details, asset valuations, and risk profiles. Designed a structured asset registry to track user assets, map them to existing policies, and automatically flag uninsured or underinsured items.
SafeEats: AI-Powered Food Allergen Detection
Developed a mobile application using AI and image recognition to improve food safety for individuals with allergies. Preprocessed and annotated image datasets using Roboflow to train a YOLOv8 object detection model for identifying allergenic ingredients in food items.
MCP Database with AI Integration
Developed a local AI-powered system integrating a quantized Qwen 2.5 3B model with SQLite, MongoDB, and PostgreSQL using FastMCP. Implemented natural language interaction for data management and analysis through a unified Python client, reducing average query time by ~20% compared to manual SQL workflows.
Wine Prediction Model
Built a predictive AI model for wine recommendations based on food pairings using data preprocessing, feature engineering, and ML algorithms. Employed NLP and Word2Vec embeddings to match wine and food flavors with rule-based logic for pairing, achieving ~82% recommendation accuracy on test data.
Education
BSCS
2019 - 2023
FAST NUCES
Lahore
Intermediate
2017 - 2019
Forman Christian College
Lahore
