Praneeth Reddy

Designed scalable Python backend frameworks with FastAPI, SQLAlchemy, and Boto3 to automate service integrations and deliver AI/ML-ready APIs, reducing manual effort by 70%. Built Python and PySpark data pipelines on AWS using S3, Glue, and EMR for large-scale batch and near-real-time processing, including validation, transformation, and performance optimization. Delivered production support for Python and AWS applications through troubleshooting, root-cause analysis, and incident resolution. Developed microservices and low-latency services using FastAPI and gRPC, integrated with Oracle and cloud services, and implemented event-driven workflows with SNS/SQS, Lambda, Step Functions, Kinesis, and asyncio to reduce latency by 60%. Containerized services with Docker and deployed on AWS Fargate; automated infrastructure and deployments with Terraform and CloudFormation; built CI/CD pipelines using Git, CodeBuild, Docker, and pytest. Also engineered reusable Python SDKs and prompt-engineeri

Built scalable backend services and REST/GraphQL APIs using FastAPI/Flask and SQLAlchemy, integrating enterprise applications and third-party SDKs to reduce API response time by 40%. Modernized legacy components by migrating workflows to scalable Python-, Spark-, and AWS-based architectures, including complex SQL optimization and operational automation with Unix/Linux scripting. Created secure event-driven Python SDKs and services using FastAPI, SNS/SQS, and OAuth2, improving availability by 35%. Implemented modular architectures with Django and SQLAlchemy, achieving backend performance gains through caching, indexing, and query tuning. Orchestrated workflows with Apache Airflow and achieved 99.9% reliability; implemented CI/CD with Git/Bitbucket, pytest, Docker, and AWS CodeBuild. Developed AI-powered applications with TensorFlow/Scikit-learn, later integrating SageMaker, OpenAI APIs, and LangChain for intelligent automation and backend integration.

Designed and delivered scalable backend services and RESTful APIs with FastAPI and Flask, using modular SDK-based architecture and improving response performance by 35%. Worked in Agile/Scrum and supported code reviews and mentoring for junior developers. Built secure data integration modules using SQLAlchemy, PyODBC, and Boto3 to connect PostgreSQL, MongoDB, and S3. Developed reusable REST APIs and automated workflows with Flask/FastAPI and AWS Lambda. Implemented asynchronous event-driven services using Lambda, SNS, and SQS for near-real-time processing; optimized performance using NumPy and multithreading. Containerized services with Docker and orchestrated deployments with Amazon EKS; automated IaC with Terraform; integrated SageMaker-based ML services for predictive scoring and decision support.