Praveen Kumar

TrendalyTix

• Architected predictive models using Scikit-learn and Pandas to analyze real-world datasets, improving data-driven decision accuracy.
• Optimized automated data cleaning pipelines for production-grade analytics workflows.
• Synthesized complex technical findings into actionable business insights, presenting stakeholders with interactive visualizations.

Outlier

• Optimized Large Language Model (LLM) performance through Advanced Prompt Engineering and Chain-of-Thought reasoning across STEM and coding domains.
• Executed RLHF (Reinforcement Learning from Human Feedback) workflows to evaluate and refine model outputs, ensuring technical accuracy and ethical alignment.

• Developed an autonomous Cognitive Agent using LangGraph that plans analysis steps, executes Python code, and autonomously resolves runtime errors via recursive reflection loops (Self-Healing).
• Deployed a secure, containerized application using Docker on Render with a Streamlit interface, implementing sandbox guardrails.

• Designed a multimodal deep learning pipeline utilizing CNNs (Encoder) for spatial feature extraction and LSTMs (Decoder) for sequential natural language generation.
• Enhanced model accuracy on the Flickr8k dataset using Transfer Learning with pre-trained vision models.

• Developed a weather forecasting model to predict future conditions by analyzing historical time-series data.
• Built an end-to-end data pipeline using Machine Learning techniques in Python and Pandas.

• Engineered a GraphRAG architecture combining Neo4j Knowledge Graphs with Vector Search to resolve complex multi-hop medical queries in unstructured clinical PDFs.
• Orchestrated Llama-3 (70B) via Groq for high-precision Named Entity Recognition (NER), mapping complex Drug-Protein-Disease relationships into a structured graph schema.