Muhammad Zahid

Senior AI/ML & Full Stack Engineer (Python, React) with 6+ years of experience designing, developing, and deploying scalable AI-powered applications and enterprise software. Strong expertise in Generative AI, LLMs, Retrieval-Augmented Generation (RAG), Agentic AI, and Model Context Protocol (MCP), combined with extensive experience in Python, React, Next.js, FastAPI, Django, Flask, FastAPI and cloud-native architectures. Proven background in end-to-end AI/ML development, modern full-stack engineering, scalable ETL/ELT pipelines, RESTful APIs, microservices, and MLOps practices.

Experienced with PyTorch, TensorFlow, Scikitlearn, LangChain, LlamaIndex, vector databases, SQL, Docker, Kubernetes, AWS, Azure, and GCP. Skilled in building production-grade AI solutions, intelligent automation, and high-performance web applications with a focus on scalability, reliability, and exceptional user experience. Open to exciting fully remote global opportunities.

Senior AI/ML Engineer - Birxment - Lahore, Pakistan

(2024-02)

Led the end-to-end design and deployment of production-grade AI/ML and Generative AI systems, including LLM-powered applications, scalable data pipelines, and real-time inference infrastructure.

• Architected end-to-end ML and LLM pipelines in Python covering preprocessing (Pandas/NumPy), training (PyTorch, TensorFlow), validation, and inference.
• Fine-tuned Large Language Models (LLMs) using transformer architectures for domain-specific applications.
• Built RAG pipelines using vector databases (FAISS/Pinecone) for context-aware AI systems.
• Developed Agentic AI workflows using LangChain/LlamaIndex for multi-step reasoning and automation.
• Applied MCP and structured prompting techniques for better context handling and response quality.
• Developed deep learning (CNNs, sequence models) and classical ML models using PyTorch, TensorFlow, and scikit-learn.
• Designed evaluation frameworks with cross-validation, metrics tracking, and hyperparameter tuning.
• Containerized models using Docker and deployed REST APIs (FastAPI) for real-time and batch inference.
• Built SQL-based ETL pipelines and automated workflows using Airflow/Kubeflow Pipelines.
• Implemented MLOps pipelines using MLflow, Kubeflow, CI/CD for model lifecycle management and versioning.
• Collaborated with cross-functional teams to productionize scalable and reliable AI systems.

AI & Machine Learning Engineer - DevSinc - Lahore, Pakistan

(2022-07 - 2024-01)

Contributed to the design and development of scalable AI/ML solutions, focusing on model development, experimentation, and production deployment across diverse use cases.

• Designed and developed machine learning models using Python (PyTorch, TensorFlow, Scikit-learn) for structured and unstructured data.
• Built end-to-end ML pipelines covering data preprocessing, training, validation, and deployment.
• Worked on Generative AI and LLM-based solutions, including fine-tuning and evaluation of transformer models.
• Implemented experiment tracking and reproducibility using MLflow and structured evaluation frameworks.
• Developed REST APIs and integrated ML models into production systems using FastAPI and Docker.
• Built and maintained ETL pipelines to support data ingestion, transformation, and ML-ready datasets.
• Collaborated with cross-functional teams to translate business requirements into scalable AI solutions.
• Optimized model performance through feature engineering, hyperparameter tuning, and evaluation metrics.
• Applied MLOps practices for model versioning, CI/CD, and deployment automation.
• Monitored model performance and improved system reliability through continuous iteration.

Machine Learning Engineer - CureMD - Lahore, Pakistan

(2021-03 - 2022-06)

Led the development of Python-based data engineering pipelines and ML-ready infrastructure supporting large-scale healthcare analytics and automated clinical reporting systems in a high-volume enterprise environment.

• Designed and implemented secure Python (Pandas/NumPy) ETL pipelines to process and transform high-volume structured medical datasets.
• Engineered ML-ready datasets through data cleaning, normalization, feature preparation, and structured transformation workflows.
• Developed backend data services and REST APIs to support automated clinical reporting and analytics integration.
• Optimized SQL queries and transformation logic to reduce processing latency and improve pipeline efficiency.
• Performed time-series data analysis to generate operational insights and performance monitoring reports.

Full Stack Engineer - Netsol Technologies - Lahore, Pakistan

(2019-09 - 2021-02)

Contributed to the design and development of Python-based backend and Web Applications supporting high-volume financial leasing platforms, with focus on scalable data processing and analytics enablement in production environments.

• Developed full stack web applications using Python (Django, Flask, FastAPI) & JavaScript (React.js).
• Developed backend services and REST APIs using Python to support enterprise financial workflows.
• Automated business operations through structured data processing pipelines and workflow orchestration.
• Designed and optimized PostgreSQL database schemas and SQL queries for high-volume transactional data.
• Collaborated in Agile/Scrum teams to deliver reliable, production-ready enterprise systems.

Bachelor's Degree - Computer Science - COMSATS Institute of Information and Technology (2014-09 - 2018-08)