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I'm Muhammad Hamza, an AI/MLOps-focused software engineer and published IEEE researcher with hands-on experience building end-to-end ML pipelines, RAG systems, and production-ready AI services using Python, SQL, FastAPI, REST APIs, PyTorch, TensorFlow, Scikit-learn, Pandas, and NumPy. I apply MLOps practices with MLflow, Weights & Biases, LangChain, FAISS/Pinecone vector search, and containerization with Docker and Kubernetes to deploy scalable models. I also emphasize monitoring with Grafana and Evidently AI, and I collaborate across teams to translate research into reliable features for real-world problems.…I'm Muhammad Hamza, an AI/MLOps-focused software engineer and published IEEE researcher with hands-on experience building end-to-end ML pipelines, RAG systems, and production-ready AI services using Python, SQL, FastAPI, REST APIs, PyTorch, TensorFlow, Scikit-learn, Pandas, and NumPy. I apply MLOps practices with MLflow, Weights & Biases, LangChain, FAISS/Pinecone vector search, and containerization with Docker and Kubernetes to deploy scalable models. I also emphasize monitoring with Grafana and Evidently AI, and I collaborate across teams to translate research into reliable features for real-world problems.

Muhammad Hamza

AI Engineer, Data Scientist, Web Developer, +2

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I'm Muhammad Hamza, an AI/MLOps-focused software engineer and published IEEE researcher with hands-on experience building end-to-end ML pipelines, RAG systems, and production-ready AI services using Python, SQL, FastAPI, REST APIs, PyTorch, TensorFlow, Scikit-learn, Pandas, and NumPy. I apply MLOps practices with MLflow, Weights & Biases, LangChain, FAISS/Pinecone vector search, and containerization with Docker and Kubernetes to deploy scalable models. I also emphasize monitoring with Grafana and Evidently AI, and I collaborate across teams to translate research into reliable features for real-world problems.…I'm Muhammad Hamza, an AI/MLOps-focused software engineer and published IEEE researcher with hands-on experience building end-to-end ML pipelines, RAG systems, and production-ready AI services using Python, SQL, FastAPI, REST APIs, PyTorch, TensorFlow, Scikit-learn, Pandas, and NumPy. I apply MLOps practices with MLflow, Weights & Biases, LangChain, FAISS/Pinecone vector search, and containerization with Docker and Kubernetes to deploy scalable models. I also emphasize monitoring with Grafana and Evidently AI, and I collaborate across teams to translate research into reliable features for real-world problems.

Available to hire

I’m Muhammad Hamza, an AI/MLOps-focused software engineer and published IEEE researcher with hands-on experience building end-to-end ML pipelines, RAG systems, and production-ready AI services using Python, SQL, FastAPI, REST APIs, PyTorch, TensorFlow, Scikit-learn, Pandas, and NumPy.

I apply MLOps practices with MLflow, Weights & Biases, LangChain, FAISS/Pinecone vector search, and containerization with Docker and Kubernetes to deploy scalable models. I also emphasize monitoring with Grafana and Evidently AI, and I collaborate across teams to translate research into reliable features for real-world problems.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Language

English

Fluent

Work Experience

AI Engineer at Excels Tech Solution LLC

November 1, 2024 - Present

Built and deployed AI workflows using Python, FastAPI, REST APIs, and SQL-backed services, moving models from experimentation into usable production features. Developed RAG-based knowledge systems with LangChain, embeddings, FAISS/Pinecone-style vector search, and prompt-engineering patterns for document Q&A and internal search use cases. Applied MLOps practices with MLflow for experiment tracking, model versioning, and reproducible handoffs across development, testing, and deployment stages. Containerized AI services with Docker and supported Kubernetes-ready deployment patterns for scalable model serving and cleaner environment management. Added practical monitoring and logging around model/API behavior using Grafana dashboards and Evidently AI for model/data drift checks.

Associate Software Engineer at Devsinc

February 1, 2022 - October 1, 2024

Built end-to-end ML pipelines in Python using Pandas, NumPy, Scikit-learn, TensorFlow, and PyTorch for preprocessing, feature engineering, model training, and evaluation. Integrated trained models into FastAPI inference services and REST API endpoints connected with PostgreSQL/MySQL data sources. Tracked model experiments, metrics, and artifacts using MLflow and Weights & Biases to compare versions and support repeatable tuning cycles. Used GitHub-based workflows and lightweight CI checks to keep Python services, notebooks, and ML pipeline updates reviewable and easier to release. Improved model performance through fine-tuning, validation, error analysis, and clear documentation of datasets, assumptions, metrics, and deployment notes.

Jr. Software Engineer at Arham Soft

July 1, 2020 - January 1, 2022

Implemented deep learning experiments with CNNs, RNNs, and Transformers using Python, PyTorch/TensorFlow, and Jupyter Notebooks for computer vision and NLP prototypes. Prepared data analysis and preprocessing workflows with Pandas, NumPy, Matplotlib, and Seaborn to clean datasets, inspect patterns, and validate model inputs. Used CUDA-enabled training environments where available to speed up experimental runs and understand GPU-based ML workflow constraints. Maintained GitHub repositories with structured Python modules, requirements files, and reusable scripts to make experiments easier to reproduce and review. Supported backend and database tasks using Java, SQL, MongoDB, PostgreSQL, and MySQL while strengthening REST API and production-style application design skills.