Haswanth Potti

About Haswanth Potti

I’m a Software Engineer with around two years of experience building and integrating AI-driven solutions into real-world applications. My work primarily focuses on Python-based development, leveraging libraries such as TensorFlow, PyTorch, Scikit-learn, and Pandas for machine learning and deep learning workflows. I have a solid understanding of core ML algorithms, data preprocessing, and model evaluation techniques. I enjoy working across the full lifecycle — from preparing and cleaning datasets using SQL and Spark to deploying models through containerized environments using Docker and basic CI/CD pipelines. I’m also familiar with AWS and Azure for managing data pipelines and deploying inference services. Beyond technical skills, I value writing clean, maintainable code and collaborating with data scientists, engineers, and product teams to translate ideas into working solutions. I’m continuously learning new tools and approaches in MLOps, cloud computing, and model optimization to grow as a well-rounded AI engineer.

Experience

1. Ai Software Engineer

   Quadrant Technologies

   Jan 2024 — Present

   As an AI Software Engineer at Quadrant Technologies, I contribute to building and deploying scalable machine learning and deep learning solutions that drive intelligent automation and data-driven insights across multiple business domains. My role involves end-to-end model development, API integration, and production deployment with a focus on reliability, performance, and MLOps best practices.Key Contributions:Developed and fine-tuned NLP and computer vision models using TensorFlow, Hugging Face Transformers, and YOLOv8, achieving measurable accuracy and latency improvements.Built and maintained end-to-end ML pipelines for training and inference using Python, Pandas, Scikit-learn, and Apache Spark, improving preprocessing efficiency by 25%.Deployed FastAPI-based microservices for serving AI models, containerized with Docker and orchestrated via AWS ECS.Supported MLOps workflows by integrating MLflow, GitHub Actions CI/CD, and model versioning for reproducible and automated retraining.Collaborated with data, backend, and DevOps teams to integrate predictive analytics into core products and ensure high availability in production.Implemented monitoring and alerting systems using AWS CloudWatch and Prometheus to track model performance and detect drift.Authored technical documentation on model architecture, deployment procedures, and data governance standards to support future scalability and compliance.

Education

• The University of Texas at Arlington

  Master's degree

• Chennai Institute of Technology

  Bachelor's degree