Manisha Ratnapur

Results-oriented Data Scientist with expertise in Artificial Intelligence, Multimodal Data Processing, and Deep Learning. Engineered AI solutions enhancing user engagement and content recommendations. Developed NLP pipelines for text classification and sentiment analysis, optimizing business decision-making. Directed cross-functional initiatives for vector database and cloud systems handling large-scale datasets.

Data Scientist - HCL TECH Pvt Ltd - Hyderabad

(2024-08)

Architected an Agentic AI Legal Assistant using Multi-Agent RAG architecture to automate legal research, statute interpretation, case summarization, compliance validation, and evidence retrieval workflows across 358 IPC sections.

• Built scalable legal AI pipelines using GPT-4, LangChain, and Transformer-based NLP models for contextual legal Q&A, grounded response generation, and intelligent document analysis.
• Coordinated specialized AI agents for legal research, summarization, and evidence retrieval to support multi-step legal reasoning and workflow automation.
• Generated semantic embeddings using OpenAI text-embedding-3-large and implemented Qdrant Vector Database with hybrid semantic retrieval and cosine similarity search for high-accuracy contextual retrieval.
• Optimized retrieval quality using RecursiveCharacterTextSplitter, metadata filtering, Multi-Query Retrieval, and BERT-based semantic re-ranking, improving contextual grounding and retrieval relevance.
• Evaluated RAG pipeline performance using RAGAS metrics, achieving 0.87 Faithfulness, 0.89 Answer Relevance, 0.88 Groundedness, and 0.81 MRR while reducing hallucinated responses through retrieval and prompt optimization.
• Applied Pydantic schema validation and structured response enforcement, reducing formatting inconsistencies by 25% and improving reliability of AI-generated legal outputs.
• Reduced manual legal research and document review effort by 40–50%, accelerating contract analysis and case review workflows through AI-driven retrieval and summarization automation.

Jr. Data Scientist - Mphasis - Hyderabad

(2023-03 - 2024-07)

Delivered an AI-powered Banking Chatbot using GPT-3/GPT-4 to streamline customer support, transaction assistance, account services, and personalized banking interactions.

• Enabled accurate query understanding through BERT-based intent classification and entity recognition pipelines, improving extraction accuracy by 18% for complex banking requests.
• Facilitated context-aware and multi-turn conversations using retrieval-augmented response generation and reinforcement learning-based dialogue management, improving recommendation relevance by 22%.
• Secured real-time banking workflows through JWT-based authentication, role-based access control, and REST API integrations while maintaining 99.5% service uptime.
• Optimized conversational routing, response handling, and prompt workflows to support high concurrent traffic with low-latency interactions; achieved 92–95% intent accuracy and 89–92% entity recognition F1-score.
• Improved operational efficiency by automating 60%+ of routine customer queries, reducing response time from minutes to seconds, and sustaining 93.7% customer satisfaction post-deployment.

Bachelor of Technology (B.Tech) - Computer Science Engineering - Princeton Institute of Engineering For Women (2023-06)