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Hi, I'm Swetha Devineni. I’ve spent the last decade building scalable web apps and AI-enabled systems as a Python full-stack developer. I design robust backends with FastAPI, Flask, and Django, and craft responsive frontends with React and modern JavaScript, delivering low-latency solutions that handle millions of transactions and real-time data streams. I enjoy translating complex business requirements into modular architectures, leading end-to-end implementations from API design to cloud deployment on AWS, Azure, and GCP. I also integrate AI/ML, LangChain, and RAG pipelines to automate processes and improve decision-making, while emphasizing security, testing, and observability.…Hi, I'm Swetha Devineni. I’ve spent the last decade building scalable web apps and AI-enabled systems as a Python full-stack developer. I design robust backends with FastAPI, Flask, and Django, and craft responsive frontends with React and modern JavaScript, delivering low-latency solutions that handle millions of transactions and real-time data streams. I enjoy translating complex business requirements into modular architectures, leading end-to-end implementations from API design to cloud deployment on AWS, Azure, and GCP. I also integrate AI/ML, LangChain, and RAG pipelines to automate processes and improve decision-making, while emphasizing security, testing, and observability.

Swetha Devineni

Full Stack Developer, AI Developer, Back-End Developer, +2

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Hi, I'm Swetha Devineni. I’ve spent the last decade building scalable web apps and AI-enabled systems as a Python full-stack developer. I design robust backends with FastAPI, Flask, and Django, and craft responsive frontends with React and modern JavaScript, delivering low-latency solutions that handle millions of transactions and real-time data streams. I enjoy translating complex business requirements into modular architectures, leading end-to-end implementations from API design to cloud deployment on AWS, Azure, and GCP. I also integrate AI/ML, LangChain, and RAG pipelines to automate processes and improve decision-making, while emphasizing security, testing, and observability.…Hi, I'm Swetha Devineni. I’ve spent the last decade building scalable web apps and AI-enabled systems as a Python full-stack developer. I design robust backends with FastAPI, Flask, and Django, and craft responsive frontends with React and modern JavaScript, delivering low-latency solutions that handle millions of transactions and real-time data streams. I enjoy translating complex business requirements into modular architectures, leading end-to-end implementations from API design to cloud deployment on AWS, Azure, and GCP. I also integrate AI/ML, LangChain, and RAG pipelines to automate processes and improve decision-making, while emphasizing security, testing, and observability.

Available to hire

Hi, I’m Swetha Devineni. I’ve spent the last decade building scalable web apps and AI-enabled systems as a Python full-stack developer. I design robust backends with FastAPI, Flask, and Django, and craft responsive frontends with React and modern JavaScript, delivering low-latency solutions that handle millions of transactions and real-time data streams.

I enjoy translating complex business requirements into modular architectures, leading end-to-end implementations from API design to cloud deployment on AWS, Azure, and GCP. I also integrate AI/ML, LangChain, and RAG pipelines to automate processes and improve decision-making, while emphasizing security, testing, and observability.

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Work Experience

Senior Python Full Stack Developer with AI at FIS

October 1, 2023 - Present

Led the design and development of a high-impact full-stack fraud detection and compliance platform handling multimillion-transaction workloads. Built low-latency backends with FastAPI and Flask to support real-time transaction validation and fraud scoring, delivering response times of 200–400 ms under high concurrency. Implemented scalable React-based dashboards for real-time fraud insights and compliance metrics. Orchestrated a microservices architecture on AWS (EKS, Lambda, S3, IAM) with Docker and Kubernetes, enabling 100K+ events/min ingestion, high availability, and fault tolerance. Built streaming pipelines with Apache Kafka and maintained PostgreSQL datasets with advanced indexing and query tuning. Integrated AI/ML components using GPT, LangChain, and RAG (FAISS, Pinecone) with PyTorch for automated regulatory document analysis and semantic search. Developed Pandas/NumPy pipelines for GB–TB-scale data, and established CI/CD with Jenkins and GitHub Actions. Implemented OAuth2

Senior Python Full Stack Developer (AI) at Progressive Insurance

June 1, 2022 - September 30, 2023

Delivered a high-throughput claims intelligence platform enabling end-to-end claim processing and fraud detection for 500K+ claims/month and 10M+ records/day. Built backend services with Django/Flask, creating robust RESTful APIs for claims validation, policy checks, and fraud scoring under heavy concurrency with sub-300 ms latency. Created interactive frontend dashboards with React/TypeScript for real-time risk assessment, improving decision speed and reducing manual reviews. Migrated and containerized services on AWS ECS/Fargate; implemented Step Functions and Lambda for distributed workflows, cutting processing time by ~45%. Optimized data access in MySQL and MongoDB with indexing and query tuning, achieving 35–50% performance gains. Deployed AI-assisted decisioning to improve fraud detection precision and reduce false positives. Established ELK-based logging, centralized monitoring, and CI/CD via GitLab, reducing release cycles and improving reliability.

Python Developer at Intel Corporation

July 1, 2021 - May 31, 2022

Built a real-time manufacturing analytics platform processing telemetry from 1,200+ IoT sensors. Implemented high-throughput data ingestion with Apache Kafka and Azure Event Hubs, handling 30K–45K events/min with low latency. Re-architected legacy batch workflows into event-driven streaming, reducing anomaly detection latency from 8–10 minutes to under 4 seconds. Implemented real-time stream processing with Azure Stream Analytics and Python consumers; stored history in Azure Data Lake Gen2 and Azure SQL. Integrated ML-based anomaly detection with scikit-learn and lightweight PyTorch models. Deployed microservices on AKS with Docker; tuned Kafka partitions and caching with Redis to maintain throughput and responsiveness. Set up comprehensive monitoring with Azure Monitor, Prometheus, and Grafana; established secure access with Azure AD and Key Vault. Collaborated with manufacturing engineers and operations teams to translate production requirements into scalable analytics solutions

Python Developer with AI at CVS Health

February 1, 2020 - June 30, 2021

Contributed to HIPAA-compliant clinical data platform integrating EHR data, claims feeds, and provider networks, processing 120K–160K records per cycle for enterprise-wide reporting. Built backend services in Python (Flask) with secure REST APIs for ingestion, transformation, and retrieval across distributed systems. Designed and implemented Azure Data Factory ETL pipelines, boosting reliability by 30%+. Used Pandas/NumPy to process 3–5 GB datasets per cycle; integrated HL7 v2 and FHIR APIs for standardized data exchange. Developed NLP pipelines with NLTK/spaCy to extract structured information from clinical notes and claims, improving data extraction accuracy by 25%. Modeled ML-based claim categorization with scikit-learn, reducing manual validation by 20%. Optimized queries in Azure SQL/PostgreSQL, cutting execution times by ~60%. Automated data workflows with Azure Data Factory and Azure Functions; implemented HIPAA-compliant security (OAuth2, RBAC, encryption). Implemented robu

Python Developer at Siemens

July 1, 2016 - August 31, 2019

Developed industrial data processing and reporting systems used across manufacturing units, enabling real-time visibility into production metrics and supporting operational monitoring across multiple plant environments. Built backend processing modules using Python (2.7/3.x) and Flask, handling ingestion, transformation, and delivery of operational data across internal applications, processing 100K records per batch with reliable throughput. Engineered scalable data ingestion and transformation pipelines, reducing manual data preparation effort by 30% and ensuring consistent data quality for reporting. Designed and optimized data workflows with Pandas, improving reporting accuracy by 25% and reducing inconsistencies. Tuned SQL queries in MySQL to improve report generation by 65%. Deployed on AWS (EC2, S3, IAM) and automated recurring data workflows with cron, reducing manual intervention by 40%. Implemented logging, monitoring, and data validation to improve reliability. Collaborated w