# Vipin Kumar

> AI/ML Engineer | GenAI, LLMs, NLP, Recommendation Systems, Predictive Modeling | Python, PyTorch, XGBoost, Spark, AWS | 6+ Years Experience Building Data-Driven, Scalable AI Solutions |  Open to New Roles

Location: Dallas-Fort Worth Metroplex, United States
Profile: https://flows.cv/vipinkumar

AI & ML Engineer with 6+ years of experience designing, developing, and deploying scalable machine learning systems for large-scale consumer platforms. 
Proven expertise in Python, PyTorch, XGBoost, LightGBM, and Apache Spark, building end-to-end ML pipelines, recommendation systems, fraud detection models, and predictive analytics solutions serving millions of users and high-volume data environments. 
Experienced in MLOps, model deployment, and cloud-based ML infrastructure using AWS, SageMaker, Docker, Kubernetes, and MLflow to support reliable production ML workflows. Strong background in feature engineering, distributed data processing, real-time inference systems, and model monitoring, delivering measurable improvements in model accuracy, latency, and business impact.

## Work Experience
### AI & ML Engineer @ Apple
Jan 2024 – Present | California, United States
•	Developed production machine learning models using Core ML, PyTorch, and XGBoost to power intelligent features across Apple Intelligence, supporting 50M+ daily inference requests with sub-100ms on-device latency.
•	Built large-scale feature engineering pipelines using Apache Spark and AWS EMR, integrating behavioral signals, usage telemetry, and privacy-preserving federated data from 340M+ active devices.
•	Designed dual-model prediction architecture using XGBoost (AUC 0.92) and LightGBM (AUC 0.85) for personalized recommendations and anomaly detection across Siri, App Store, and Apple Pay services.
•	Implemented model explainability using SHAP aligned with privacy-first AI principles, generating transparent model insights for ML governance, risk analysis, and compliance auditing.
•	Engineered fraud and anomaly detection system using PyTorch autoencoders and LightGBM, analyzing 85M+ Apple Pay and App Store transactions, reducing false positives by 40% while maintaining PCI-DSS regulatory compliance.
•	Built scalable MLOps infrastructure using AWS SageMaker, Apache Airflow, MLflow, Docker, and Kubernetes, enabling automated model training, deployment, versioning, and data drift monitoring using PSI metrics.

### Machine Learning Engineer @ Flipkart
Jan 2018 – Jan 2022 | India
•	Built recommendation ranking models using LightGBM (LambdaMART) in a two-stage personalization system processing 200M+ user-item interactions, improving homepage CTR by 23% and add-to-cart conversions by 11%.
•	Developed scalable feature engineering pipelines with PySpark, Hadoop, and Hive processing 15M+ daily clickstream events, generating 300+ behavioral features, user embeddings, and item affinity scores.
•	Implemented real-time ML inference infrastructure using FAISS, Redis feature store, and Kubernetes microservices, supporting 1.2M+ recommendation API requests per minute with p99 latency under 85ms.
•	Designed Customer Lifetime Value (CLV) prediction models using LightGBM and XGBoost, scoring 200M+ users weekly, improving targeted marketing campaign efficiency by 28%.
•	Automated end-to-end MLOps pipelines using Apache Airflow, Docker, and Azure HDInsight, enabling scheduled model retraining, feature computation, and batch inference workflows.

### Python Developer @ HCLTech
Jan 2017 – Jan 2018 | India
•	Developed RESTful APIs using Python, Django, and Django REST Framework with PostgreSQL for an inventory management platform managing 80,000+ SKUs across multiple warehouses.
•	Built event-driven backend services using Celery and Redis for asynchronous task processing, eliminating 4-hour batch processing delays through real-time updates.
•	Implemented concurrency-safe inventory reservation logic using PostgreSQL transactions and Django select_for_update(), preventing race conditions during payment and reservation workflows.
•	Optimized PostgreSQL database performance through indexing, query tuning, and materialized views, improving API response times from 2+ seconds to under 100ms on datasets exceeding 5M records.
•	Deployed containerized Python applications using Docker and implemented unit and integration testing with pytest, maintaining 80%+ test coverage and improving code quality and deployment reliability.


## Education
### Master of Science - MS in Business Analytics and Artificial Intelligence
The University of Texas at Dallas


## Contact & Social
- LinkedIn: https://linkedin.com/in/vipinkumar101

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Source: https://flows.cv/vipinkumar
JSON Resume: https://flows.cv/vipinkumar/resume.json
Last updated: 2026-04-17