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I am Ragapriya Kovvuri, an AI/ML Engineer with 5+ years of experience building enterprise Generative AI, recommendation systems, and intelligent search platforms at Amazon and Adobe. I specialize in LLM applications, Retrieval-Augmented Generation (RAG), semantic search, product discovery, and AI-powered recommendation systems using Amazon Bedrock, SageMaker, and transformer-based models. I have a strong background in Python, PyTorch, TensorFlow, Spark, Kubernetes, Docker, and MLOps for cloud-native AI applications. I thrive on delivering secure, scalable AI solutions that improve customer engagement and content discovery. I enjoy collaborating with cross-functional teams to translate advanced AI capabilities into measurable business impact, from designing guardrails and security measures to building scalable inference infrastructures that handle thousands of requests and reduce latency. My work spans end-to-end AI pipelines, including automated evaluation, governance, and production-grade deployment, with a track record of delivering concrete improvements in search relevance, recommendations, and user engagement.…I am Ragapriya Kovvuri, an AI/ML Engineer with 5+ years of experience building enterprise Generative AI, recommendation systems, and intelligent search platforms at Amazon and Adobe. I specialize in LLM applications, Retrieval-Augmented Generation (RAG), semantic search, product discovery, and AI-powered recommendation systems using Amazon Bedrock, SageMaker, and transformer-based models. I have a strong background in Python, PyTorch, TensorFlow, Spark, Kubernetes, Docker, and MLOps for cloud-native AI applications. I thrive on delivering secure, scalable AI solutions that improve customer engagement and content discovery. I enjoy collaborating with cross-functional teams to translate advanced AI capabilities into measurable business impact, from designing guardrails and security measures to building scalable inference infrastructures that handle thousands of requests and reduce latency. My work spans end-to-end AI pipelines, including automated evaluation, governance, and production-grade deployment, with a track record of delivering concrete improvements in search relevance, recommendations, and user engagement.

Ragapriya Kovvuri

AI Engineer, Data Scientist, Full Stack Developer, +5

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I am Ragapriya Kovvuri, an AI/ML Engineer with 5+ years of experience building enterprise Generative AI, recommendation systems, and intelligent search platforms at Amazon and Adobe. I specialize in LLM applications, Retrieval-Augmented Generation (RAG), semantic search, product discovery, and AI-powered recommendation systems using Amazon Bedrock, SageMaker, and transformer-based models. I have a strong background in Python, PyTorch, TensorFlow, Spark, Kubernetes, Docker, and MLOps for cloud-native AI applications. I thrive on delivering secure, scalable AI solutions that improve customer engagement and content discovery. I enjoy collaborating with cross-functional teams to translate advanced AI capabilities into measurable business impact, from designing guardrails and security measures to building scalable inference infrastructures that handle thousands of requests and reduce latency. My work spans end-to-end AI pipelines, including automated evaluation, governance, and production-grade deployment, with a track record of delivering concrete improvements in search relevance, recommendations, and user engagement.…I am Ragapriya Kovvuri, an AI/ML Engineer with 5+ years of experience building enterprise Generative AI, recommendation systems, and intelligent search platforms at Amazon and Adobe. I specialize in LLM applications, Retrieval-Augmented Generation (RAG), semantic search, product discovery, and AI-powered recommendation systems using Amazon Bedrock, SageMaker, and transformer-based models. I have a strong background in Python, PyTorch, TensorFlow, Spark, Kubernetes, Docker, and MLOps for cloud-native AI applications. I thrive on delivering secure, scalable AI solutions that improve customer engagement and content discovery. I enjoy collaborating with cross-functional teams to translate advanced AI capabilities into measurable business impact, from designing guardrails and security measures to building scalable inference infrastructures that handle thousands of requests and reduce latency. My work spans end-to-end AI pipelines, including automated evaluation, governance, and production-grade deployment, with a track record of delivering concrete improvements in search relevance, recommendations, and user engagement.

Available to hire

I am Ragapriya Kovvuri, an AI/ML Engineer with 5+ years of experience building enterprise Generative AI, recommendation systems, and intelligent search platforms at Amazon and Adobe. I specialize in LLM applications, Retrieval-Augmented Generation (RAG), semantic search, product discovery, and AI-powered recommendation systems using Amazon Bedrock, SageMaker, and transformer-based models. I have a strong background in Python, PyTorch, TensorFlow, Spark, Kubernetes, Docker, and MLOps for cloud-native AI applications. I thrive on delivering secure, scalable AI solutions that improve customer engagement and content discovery.

I enjoy collaborating with cross-functional teams to translate advanced AI capabilities into measurable business impact, from designing guardrails and security measures to building scalable inference infrastructures that handle thousands of requests and reduce latency. My work spans end-to-end AI pipelines, including automated evaluation, governance, and production-grade deployment, with a track record of delivering concrete improvements in search relevance, recommendations, and user engagement.

Skills

Experience Level

Expert

Expert

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Expert

Intermediate

Language

English

Fluent

Work Experience

AI/ML Engineer at Amazon

November 1, 2023 - Present

Architected a Generative AI shopping assistant using Amazon Bedrock, LLMs, Retrieval-Augmented Generation (RAG), and vector search to enable conversational product discovery and improve recommendation relevance by 32%. Developed hybrid retrieval pipelines combining semantic search, BM25, product metadata filtering, embeddings, and cross-encoder reranking, increasing search precision by 35% and reducing irrelevant recommendations. Built scalable ingestion and indexing pipelines using Python, Spark, SageMaker, and OpenSearch to process millions of product listings and cut indexing latency by 45%. Engineered intelligent recommendation pipelines leveraging browsing history, purchase behavior, contextual signals, and LLM-based reasoning, increasing click-through rates by 18% and conversion rates by 12%. Implemented automated LLM evaluation frameworks (RAGAS, LangSmith) and offline datasets to measure answer quality, groundedness, retrieval effectiveness, and hallucination rates, reducing un

AI/ML Engineer at Adobe

February 1, 2019 - March 1, 2022

Architected AI solutions using PyTorch, TensorFlow, and Transformer-based models to automate digital asset tagging, metadata generation, and semantic content understanding, improving asset discoverability by 32%. Developed computer vision and NLP pipelines for image classification, object detection, OCR, and keyword extraction, reducing manual tagging effort by 60% and improving metadata accuracy by 30%. Built semantic search systems leveraging vector embeddings, FAISS, Elasticsearch, and hybrid retrieval, reducing asset search time by 40% and increasing search relevance by 28%. Implemented end-to-end MLOps pipelines using MLflow, Docker, Kubernetes, and Azure ML, automating model training, deployment, monitoring, and retraining while reducing release cycles by 50%. Integrated AI-powered tagging and semantic search capabilities into AEM and DAM solutions within Adobe Experience Manager, improving enterprise content retrieval efficiency by 35%.