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Shreya Kotha

@shreyakotha

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AI & Machine Learning Engineer specializing in LLM integration, RAG systems, and scalable production ML infrastructure.

United States

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What I'm looking for

I seek roles building scalable, production LLM and ML systems with cross-functional impact, strong engineering practices, CI/CD, observability, and opportunities to optimize model performance.

I am an AI and Machine Learning Engineer with hands-on experience designing and deploying production-grade LLM integrations, RAG pipelines, and low-latency APIs. At Amazon, I delivered a RAG system that improved response relevance by 28% and reduced retrieval latency by 35%, applied LoRA fine-tuning to boost domain accuracy by 15%, and built CI/CD-deployed microservices for sub-200 ms inference.

My background spans ML engineering, data engineering, and software development across cloud-native environments using tools like LangChain, FAISS, Pinecone, PyTorch, Docker, Kubernetes, and AWS. I collaborate cross-functionally to translate user needs into deployed AI features, optimize model performance, and implement observability for reliable production systems.

Experience

Work history, roles, and key accomplishments

Current

Machine Learning Engineer

Current

Amazon

Dec 2024 - Present (10 months)

Designed and deployed RAG pipelines and LLM integrations that improved response relevance by 28% and reduced retrieval latency by 35%, and implemented LoRA fine-tuning to raise domain accuracy by 15% while delivering sub-200 ms inference APIs.

RAG Faiss Pinecone LoRAs fastAPI Docker Kubernetes

Software Engineer

Siemens Digital Industries Software

Feb 2023 - Nov 2024 (1 year 9 months)

Developed data-processing APIs and integrated PyTorch anomaly-detection models to improve fault-prediction accuracy by 18%, and optimized multi-GPU training and containerized deployments for reliability.

PyTorch Distributed Training Docker Kubernetes Grafana CI CD

Data Engineer

Triant Software Solutions

Sep 2020 - May 2021 (8 months)

Built Spark and Kafka ETL pipelines and SQL-driven preprocessing that increased data-processing throughput by 25% and supported forecasting and churn models that improved accuracy by 20%.