# Akshat Gurbuxani > MS AI @ BU | Ex-MLE @ Optum | Ex-Data Scientist @ Boston Public School | Ex-Data Scientist @ Boston Universty | Machine Learning Engineer Location: United States, United States Profile: https://flows.cv/akshatgurbuxani Machine Learning Engineer with a track record of designing and deploying scalable AI/ML solutions across NLP, computer vision, and predictive modeling. I specialize in building intelligent systems that drive automation, optimize performance, and enhance decision-making. At Boston Public Schools, I developed a RAG-based chatbot integrated with FAISS, vector search, and a multi-agent LLM pipeline, automating policy retrieval and reducing manual effort by 85%. At Optum, I productionized a neural collaborative filtering model for personalized insurance recommendations, boosting user retention by 70%. I also optimized ML pipelines with Apache Spark, reducing training time by 75%, and deployed time-series forecasting models for traffic prediction with 80% accuracy. Beyond industry roles, my research in 3D human pose estimation leveraged Spatiotemporal Transformers and LSTMs, achieving a 55mm MPJPE on Human3.6M. My work in medical image segmentation implemented U-Net with CrossBlock and few-shot learning, improving diagnostic accuracy by 40%. I thrive at the intersection of AI, data engineering, and scalable systems, leveraging Python, SQL, Spark, AWS, Docker, and Kubernetes to deploy end-to-end ML pipelines. Experienced in MLOps, GenAI, and LLM-based solutions, focusing on building scalable AI systems, optimizing deployment workflows, and integrating automation for efficient model lifecycle management. Silver medalist in Kaggle’s AI Mathematical Olympiad (AIMO), applying Chain-of-Thought reasoning, and fine-tuning on DeepSeekMath LLM to solve advanced mathematical problems. Let’s connect and talk AI, data, and cutting-edge ML innovations. ## Work Experience ### Software Engineer (Machine Learning) @ Semiotic Labs Jan 2025 – Present | Los Altos, CA • Trained SOTA multi-modal NFT spam detection system across 8 blockchain networks using fine-tuned LLM, GNN, XGBoost with Mixture-of-Experts ensemble, achieving 87% accuracy, correctly flagging 150,000+ malicious contracts • Built trajectory-aware multi-agent pipeline with DSPy ReAct agents, integrating The Graph’s Token API and Subgraphs with judge-agent for evaluation; improved response accuracy by 70%, reduced query latency by 60% in production ### Machine Learning Intern @ Boston Public Schools Jan 2024 – Jan 2024 | Boston, MA • Designed RAG-based chatbot with BPS policy repository, automating document checks and reducing manual effort by 85% • Aligned FAISS with vector and lexical search, metadata-driven clustering, and a multi-agent LLM pipeline, enhancing retrieval speed by 70% and document relevance by 90% ### Teaching Assistant - CS505 Grad Intro to Natural Language Processing (NLP) @ Boston University Jan 2024 – Jan 2024 | Boston, Massachusetts, United States • Lectured and mentored ~90 graduate students in NLP, covering transformers, sequence models, and embeddings while guiding the implementation and evaluation of modern architectures ### Full-stack Developer (LERNet) @ Boston University Jan 2024 – Jan 2024 | Boston, Massachusetts, United States • Optimized and updated LERNet, AI4ALL, and Artemis websites, delivered lectures, and integrated a Makeblock robotics course to strengthen the curriculum with hands-on STEM and robotics learning ### Course Assistant - CS506 Grad Data Science Tools and Applications @ Boston University Jan 2024 – Jan 2024 | Boston, Massachusetts, United States • Assisted in teaching CS506, guiding ~150 graduate students through core ML topics like regression, classification, clustering, decision trees, and model evaluation, while providing hands-on support in data preprocessing and algorithm implementation ### Data Science Intern @ Boston University Jan 2023 – Jan 2023 | United States • Delivered regression-based predictive models using performance data and past grades from 8000 athletes, achieving 90% accuracy in forecasting future grades, reducing dropout rates by 15%, and increasing athlete retention by 20% • Deployed Power BI dashboard for benchmarking athlete performance against other teams, enabling coaches and managers to identify top-performing athletes, improving training efforts by 25%, and team success rates by 15% ### Machine Learning Engineer @ Optum Jan 2021 – Jan 2023 | Hyderabad, Telangana, India • Productionized a neural collaborative filtering model to personalize insurance policy recommendations, increasing retention by 70% and page traffic by 86%. Integrated Kafka and RESTful APIs for real-time content delivery • Optimized ML pipelines using Apache Spark, reducing model training time by 75%, enabling faster model iterations and scalable deployments • Developed an LSTM-based time-series forecasting model for website traffic prediction, attaining 80% accuracy. Automated testing workflows with pytest, Docker, CI/CD, Jenkins, and Kubernetes reducing manual testing by 70% • Engineered distributed data pipeline using Apache Kafka and Apache Flink for real-time data processing, reducing data latency from 40 minutes to 5 minutes, and increasing data throughput by 80% • Structured multi-node model deployment architecture using Kubernetes and Docker, enabling parallel inference across multiple GPUs, reducing inference time by 70%, supporting high-throughput applications ## Education ### Master's degree in Artificial Intelligence Boston University ### Bachelor of Technology - BTech in Information Technology SRM IST Chennai ### HSC Emmanuel Mission ### SSC Rani Laxmi Bai Public school ## Contact & Social - LinkedIn: https://linkedin.com/in/akshatgurbuxani --- Source: https://flows.cv/akshatgurbuxani JSON Resume: https://flows.cv/akshatgurbuxani/resume.json Last updated: 2026-04-05