Vinay Karakavalasa

AI Researcher

Andhra Pradesh, India0 mo experience

AI EnabledAI ML Practitioner

Key Highlights

3+ years of experience in AI and NLP
Expert in developing scalable AI solutions
Proficient in rapid prototyping and model optimization

Stackforce AI infers this person is a skilled AI and NLP engineer with a focus on developing intelligent systems.

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Skills

Core Skills

Natural Language Processing (nlp)Machine Learning

Other Skills

Agentic AIAmazon Web Services (AWS)Artificial Intelligence (AI)C (Programming Language)Core JavaDeep LearningFastAPIJavaLangChainLangGraphMachine Learning AlgorithmsMicrosoft AzureModular CodingPyTorchPython

Experience

Tata consultancy services

Research & Innovation Engineer

May 2022 – Present · 3 yrs 10 mos · Hyderabad, Telangana, India

Build various AI driven solutions in the form of POC's across the field of NLP and SpaCy for DataPlus and Data Transformation teams to achieve business requirements.
☐ Built an NLP-driven tool using SpaCy’s en-core-web-lg for intelligent schema recognition and automated data transformation workflows.
☐ Designed and implemented a Corrective Retrieval-Augmented Generation pipeline using LangGraph, GPT-4o, and text-embedding-3 to enhance semantic retrieval and documentation navigation.
☐ Achieved 94% Context Precision and 91% Answer Relevancy, ensuring highly accurate, context-aware responses.
☐ PII Detection System: Developed an end-to-end ML pipeline to classify database column metadata as PII/Non-PII, achieving an average accuracy of 85.78%.

Machine Learning AlgorithmsPython (Programming Language)spaCyNatural Language Processing (NLP)Retrieval-Augmented Generation (RAG)Artificial Intelligence (AI)+8

Tcs digital

Research And Development Intern

Mar 2022 – May 2022 · 2 mos · India · Remote

Developed a Data Abstractive Summary Tool to automatically generate concise summaries from news articles using a fuzzy-logic-based sentence ranking method.
Roles & Responsibilities:
1. Used various Fuzzy-based ranking methods to score the sentences from the given article data.
2. Enabled users to customize summary length by selecting the desired number of sentences for each article’s summary, offering flexibility and control over the output.
3. Integrated the generated summaries with Pegasus Paraphraser, a HuggingFace Generative AI model, to enhance readability and coherence of abstractive summaries.
Results:
Automated data abstraction, eliminating manual effort and reducing time spent on summarizing large content, enhancing user productivity.

Natural Language Processing (NLP)Pythonstreamlit