Waqas Abbas

Senior Software Engineer @ RemoteReps

About

I’m a Senior Software Engineer with 8+ years of experience in designing, developing, and deploying AI-powered, full-stack applications using Python, Machine Learning, and cloud-native technologies. My focus is on building intelligent, scalable systems that bridge software engineering, machine learning, and data-driven decision-making. I specialize in end-to-end AI/ML development, including data preprocessing, model training, deployment, monitoring, and optimization using MLOps best practices. Skilled in architecting robust backends with FastAPI, Django, and Flask, and integrating ML models into production through CI/CD pipelines, Docker, and Kubernetes. My expertise extends to AWS Cloud, where I design and maintain infrastructure leveraging Lambda, ECS, EKS, S3, DynamoDB, API Gateway, and SageMaker for scalable and cost-efficient solutions. I’m passionate about building reliable ML systems that deliver measurable business impact—combining modern software architecture, data pipelines, and AI automation. Technical Skills: Python | FastAPI | Django | Flask | React | JavaScript | TensorFlow | PyTorch | Scikit-Learn | LangChain | NLP | LLMs | Deep Learning | MLOps | MLflow | DVC | Airflow | AWS (Lambda, S3, EC2, ECS, EKS, DynamoDB, API Gateway, SageMaker) | Docker | Kubernetes | CI/CD | Microservices | Data Engineering | Cloud Architecture

Country

Pakistan

City

Lahore

Industry

Computer Software

Skill

Leadership, Cross-team Collaboration, MLOps, Model Development, fast api, Data Pipelines, Zoho, Large Language Models (LLM), Prompt Engineering, Microservices, Architecture, Cloud Applications, AWS SageMaker, Scikit-Learn, TensorFlow, lamda, Kubernetes, pandas, Application Engineering, Django REST Framework

Experience

Senior Software Engineer

RemoteReps

2023-3 - Present · 3 yrs 7 mos

United States

Leading the design and development of AI-powered web solutions, integrating machine learning models into scalable production environments. Driving end-to-end MLOps adoption, from model training and validation to automated deployment and monitoring, ensuring faster experimentation and reliable rollouts. Collaborating with stakeholders and cross-functional teams to define technical roadmaps and AI-driven product strategies aligned with business goals. Mentoring junior developers on Python best practices, code scalability, and cloud-native development workflows. Contributing to full stack architecture using FastAPI, React, and AWS to deliver data-intensive, real-time applications with strong performance and maintainability.

Senior Software Engineer

The Incc

2020-1 - 2023-2 · 3 yrs 2 mos

Led the design and implementation of intelligent backend systems, integrating LLM-based features and custom NLP pipelines for advanced text understanding and automation. Developed data processing and model-serving layers to handle high-volume requests efficiently, ensuring low-latency inference and robust API performance. Built internal tools for feature engineering, data validation, and monitoring, improving model reliability and deployment consistency across environments. Streamlined CI/CD for ML models through container orchestration and continuous delivery frameworks, enhancing reproducibility and reducing release friction. Collaborated with cross-functional teams to translate business requirements into scalable AI-powered solutions, driving automation and measurable operational gains.

Associate Software Engineer

Devsinc

2017-3 - 2019-12 · 2 yrs 10 mos

Lahore, Punjab, Pakistan

Designed and deployed AI-powered applications using Python, FastAPI, and LangChain, integrating LLMs and vector databases for real-time, context-aware responses. Developed end-to-end machine learning pipelines, including data preprocessing, model training, validation, and deployment with Scikit-Learn, TensorFlow, and PyTorch. Automated model versioning and monitoring through MLOps practices using MLflow, Docker, and AWS SageMaker, improving delivery speed and model reliability. Architected and maintained cloud infrastructure on AWS (Lambda, EC2, DynamoDB, S3), ensuring 99.9% uptime and optimized resource utilization. Collaborated cross-functionally with data and product teams to deploy ML models into production, reducing manual operations and boosting efficiency by 30%.