Arpit Rathi

Machine Learning Engineer with 7+ years of experience in applied ML, deep learning, NLP, MLOps, data science & engineering. Proven track record of delivering business impact through innovative, customer-focused software products and solutions across diverse industry domains, including AIOps, risk management, EX/CX Tech, and EdTech. Skilled at design, development, optimization, and deployment of scalable end-to-end ML systems and data pipelines.

Machine Learning Specialist at Startups [Entrepreneurship + Freelance] (2023-04 – Present)

• Founded an AI-powered note-taking app for project knowledge management, led team assembly, formulated business model, conducted user, product, & market research, designed UI/UX wireframes, and crafted a pitch deck.
• Led the development of PoCs for NLP pipelines, including semantic search, zero-shot classification, paragraph ordering, and auto-tagging.
• Architected a PoC for an end-to-end streaming data pipeline with Kafka, Zookeeper, Schema Registry, Avro, and Spark Structured Streaming for real-time processing of network data, reducing latency by 90%, and developed an HTTP tunneling attack detection pipeline with 0.95 F1-score.
• Prototyped a content recommendation system optimizing for relevance and diversity, for a B2C application focused on mental health & wellness.

Lead Data Engineer (Machine Learning) at Oliver Wyman [B.U. – Data Monetization Unit] (2022-06 – 2023-03)

• Conceived and pitched three innovative product ideas—usage-based insurance, credit portfolio monitoring, and investment risk appetite—forecasting a combined revenue potential exceeding $50M; conducted product development research, crafted persuasive pitch decks, and secured stakeholder buy-in.
• Developed a PoC for driver-risk scoring, integrating driver telematics and credit data using PySpark, and achieved 5–7x lift in claim frequency predictions at individual and aggregated levels, enabling accurate risk stratification.
• Performed rate vs. mix attribution analysis using Experian's credit database, collaborated on module design & development for advanced climate credit risk modeling, and cloud deployment of the model validation toolkit.

Manager - Machine Learning at Heal Software [Appnomic Systems] (2020-04 – 2022-04)

• Spearheaded the ML R&D for AIOps solutions, including anomaly detection, capacity forecasting, alert correlation, root cause analysis, and preventive action recommendations.
• Reduced alert volumes by up to 70% by implementing a logistic auto-regressive model for causal dependency graph mining from multivariate time-series data and grouping correlated alerts during real-time inference.
• Implemented Regression-Enhanced RF and XGBoost models for efficient extrapolation in IT infrastructure resource capacity forecasting, achieving a 60–80% reduction in RMSE scores for high-throughput scenarios.
• Improved performance by 50% in a clustering-based implementation of the patented load-behavior correlation methodology for outlier detection by introducing a probabilistic regression model.
• Designed a novel online outlier detection algorithm that reduced memory utilization by over 90% and eliminated frequent model retraining requirements, thereby enhancing system efficiency and scalability.
• Refactored the core ML module (25+ classes) to align with Scikit-Learn standards, introduced state-of-the-art ML algorithms, and a model-agnostic data preprocessing pipeline for workflow optimization.
• Advanced the company's intellectual property portfolio by filing three patent applications — an end-to-end proactive healing system, an application performance and customer experience scoring system, and an accelerated anomaly detection system for new cloud environments.

Machine Learning Engineer at Emtropy Labs [Support Logic] + Freelance (2018-01 – 2020-03)

• Fine-tuned BERT transformer-based models for multi-label classification, achieving an AUC score above 0.90; implemented aspect-based sentiment analysis of professional competencies using peer-to-peer feedback data, along with an XAI model, and deployed the models on the cloud.
• Established the product foundation by designing a novel methodology and implementing a PoC for a customer service QA automation solution, extracting customer intent & emotion, and corresponding behavioral competencies from CSR responses for objective assessment of agent performance while ensuring solution explainability.
• Delivered impactful solutions for synthetic data generation for automated number plate recognition, Bayesian networks-based anomaly detection on VLSI systems design data, and toxic content classification projects.

R&D Engineer (Machine Learning) at Next Education India Pvt. Ltd. (2017-07 – 2017-11)

• Engineered PoC for an automated grammatical error correction tool for K-12 students, achieved 80% accuracy on preposition & determiner prediction tasks using contextual feature extraction and classification.

Dual Degree in B.Tech: Electrical Engineering + M.Tech: Microelectronics – Indian Institute of Technology, Bombay (2012 – 2017)

Exchange Scholar in Electrical and Electronics Engineering – Boğaziçi University (2015 – 2016)