Vetted Regression Analysis Professionals

“Built and deployed a production bilingual (Bengali/English) AI virtual assistant that replaced IVR for telecom customer service at massive scale (~15M users), integrating ASR/TTS, Rasa dialogue management, and custom NLP. Overcame low-resource Bengali data and noisy call-center audio with synthetic data augmentation and transformer fine-tuning, achieving significant production gains including ~50% reduction in support calls.”

“Backend/ML engineer with deep fintech and marketplace experience: built a real-time financial analytics + algorithmic trading platform (Python/Postgres/Kafka/Redis) and drove major DB performance wins (10x faster analytics; sub-10ms response consistency). Also shipped an end-to-end ML recruitment matching platform (scraping/ETL/modeling/Django deployment) with reported 92% matching accuracy, and emphasizes production reliability via monitoring, blue-green deploys, and robust workflow error handling.”

“Robotics software learner building a self-directed recycling robot project in Isaac Sim, integrating ROS2 + Nav2 + SLAM with camera/LiDAR sensing and CV-based object detection. Has prior hands-on ROS2 work creating a YOLO detection node visualized in RViz and has built/optimized simulated line-follower and maze-solver robots in Webots, documenting progress publicly on GitHub and LinkedIn.”

“Built a production automated resume matching system using Python, FAISS vector search, and Selenium-based job scraping, including mitigation for IP blocking and heterogeneous site structures. Also develops LLM/RAG applications with LangChain, using Pydantic-guardrailed structured outputs and LLM-as-a-judge evaluation (including a project focused on tone/semantics for a 3D avatar’s emotional responses).”

“Console-focused QA tester with hands-on PlayStation (PS4/PS5) functional/compliance testing experience, including TRC-driven validation, stability checks, controller input testing, and performance dip investigation in combat-heavy scenarios. Also tested a Nintendo casual puzzle-adventure title and uses AI assistants to speed up crash/log analysis and produce clearer test cases and bug reports.”

“Built and deployed a production RAG chatbot using a vector database + LangChain-orchestrated pipeline, focusing on grounded, context-aware responses. Demonstrates practical trade-off thinking (retrieval quality vs latency/cost), hallucination control, and iterative improvement through logging, manual review, and stakeholder feedback loops.”