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Vamshi Krishna

@vamshikrishna3

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Backend engineer specializing in Python, FastAPI, AWS, and AI-driven scalable systems.

India

Message

What I'm looking for

I seek backend/AI roles where I can build scalable, secure cloud-native systems, apply ML and RAG techniques, work cross-functionally with product and leadership, and mentor engineers while delivering measurable business impact.

I am a backend engineer with 4+ years of experience building AI-enabled, cloud-native systems using Python, FastAPI, and AWS. I focus on designing secure, scalable architectures that enable data-driven decision-making.

At recent roles I built a RAG-based AI chatbot that dynamically generated and executed secure SQL, improved query performance by 40%, and delivered a real-time data visualization platform used by leadership to monitor operations for 6,000+ employees, reducing manual reporting by 60%.

I combine hands-on machine learning, signal processing, and cloud expertise with strong engineering practices—CI/CD, containerization, and API design—to deliver production systems. I enjoy mentoring, customer-facing collaboration, and driving measurable impact through automation and reliable infrastructure.

Experience

Work history, roles, and key accomplishments

Current

Senior Software Engineer

Current

UST Global

Feb 2024 - Present (2 years 4 months)

Architected and developed a scalable FastAPI backend enabling an AI-powered RAG chatbot to query enterprise data and execute secure SQL, improving query performance by 40%. Built a real-time visualization platform used by leadership for 6,000+ employees, reducing manual reporting overhead by 60% and deployed the solution on AWS with LLM-driven SQL sanitization.

Python fastAPI AWS RAG SQL Database Design Docker

Software Engineer

Weathernews Inc

Oct 2020 - Jan 2024 (3 years 3 months)

Built and deployed a FastAPI-based SaaS data visualization and notification platform for weather data, dockerized on AWS ECS and automating 70% of data ingestion to DynamoDB and S3. Led middleware and validation layers, applied signal processing to extract sensor data, and load-tested APIs to implement optimal scaling.