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Cerence
Data Scientist
June 18, 2026 – Present
LoRA_fine_tuning_on_Bloom
March 20, 2026 – Present
LoRA fine-tuning on BLOOM 1B using HuggingFace PEFT — demonstrates low-rank adapter injection, trainable parameter reduction (~0.1%), and efficient adaptation of a 1B-parameter model.
View ProjectFine-Tuning-Large-Language-Models
March 19, 2026 – Present
End-to-end fine-tuning of a pre-trained LLM on a customer experience dataset — instruction formatting, training with HuggingFace Trainer, and before/after inference comparison.
View ProjectLlama_Index_Query_Engine
March 18, 2026 – Present
Advanced QA agent using LlamaIndex's SubQuestionQueryEngine — decomposes complex multi-part queries into sub-questions, retrieves targeted context per sub-question, and synthesises a unified grounded answer.
View ProjectInsurance_RAG_agent
March 17, 2026 – Present
A RAG-based QA system for insurance policy documents using OpenAI embeddings, ChromaDB vector search, CrossEncoder re-ranking, and GPT response generation.
View ProjectBERT_finetuning
March 10, 2026 – Present
Fine-tuning a pre-trained BERT model for sentence pair classification tasks including NLI and semantic textual similarity, using the Hugging Face Transformers library and PyTorch.
View ProjectMLOPS_OpenSource_Pipeline
July 10, 2025 – August 24, 2025
A project demonstrating how to deploy an AI pipeline using open source tools: Airflow, MLFlow, Docker-Compose, FastApi
View ProjectBike-Share-Neural-Network
February 8, 2017 – Present
Feedforward neural network built from scratch in NumPy — manual forward pass, backpropagation, and gradient descent — to predict bike-sharing demand. No frameworks used (Udacity Deep Learning Nanodegree).
View ProjectWeatherBuddy
April 13, 2016 – Present
Android weather app with 14-day forecasts, Google Maps, GPS location, OpenWeatherMap API, and two home screen widgets (today + weekly).
View ProjectCultural Fit Analysis
The candidate's projects demonstrate a strong focus on cutting-edge AI/ML technologies, particularly in NLP and LLMs, which aligns well with an innovative, research-driven culture. The diversity of projects, from foundational neural networks to MLOps and advanced RAG systems, indicates a broad interest and willingness to explore different facets of data science. However, the projects are predominantly personal, and there is limited information on collaborative work or contributions to open-source communities beyond personal projects, which could be a factor in cultural fit for highly collaborative environments.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a proactive and self-driven approach to learning and applying advanced ML concepts. The MLOps project suggests an understanding of deployment and operational aspects of ML solutions. However, without specific assessment data on communication, logical reasoning, or teamwork, a comprehensive evaluation of soft skills and operational fit is limited.