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Hi, I’m David Natsvlishvili, an AI engineer with 16 years of experience designing, scaling, and deploying machine learning and generative AI systems across defense, enterprise, and global consumer platforms. I thrive at the intersection of research and engineering, turning cutting-edge ideas into production-ready solutions that drive measurable impact. Currently a Senior AI Engineer at Meta, I lead large-scale initiatives in LLaMA, RAG, and multimodal embeddings, and I develop PyTorch-based distributed training pipelines. I’ve built internal AI platforms that enable billions of daily predictions and created GenAI tools that let product teams experiment with LLMs in production workflows. Previously at Sutherland, I built predictive analytics and NLP solutions for enterprise clients in finance and telecom, deploying fraud detection, churn prediction, and intent classification systems. I started my career at Leidos as a Data Scientist, applying statistical modeling and anomaly detection to defense programs while creating ETL pipelines and dashboards for analysts. I’m passionate about bridging AI research and engineering execution and mentoring teams to scale infrastructure and realize business value.…Hi, I’m David Natsvlishvili, an AI engineer with 16 years of experience designing, scaling, and deploying machine learning and generative AI systems across defense, enterprise, and global consumer platforms. I thrive at the intersection of research and engineering, turning cutting-edge ideas into production-ready solutions that drive measurable impact. Currently a Senior AI Engineer at Meta, I lead large-scale initiatives in LLaMA, RAG, and multimodal embeddings, and I develop PyTorch-based distributed training pipelines. I’ve built internal AI platforms that enable billions of daily predictions and created GenAI tools that let product teams experiment with LLMs in production workflows. Previously at Sutherland, I built predictive analytics and NLP solutions for enterprise clients in finance and telecom, deploying fraud detection, churn prediction, and intent classification systems. I started my career at Leidos as a Data Scientist, applying statistical modeling and anomaly detection to defense programs while creating ETL pipelines and dashboards for analysts. I’m passionate about bridging AI research and engineering execution and mentoring teams to scale infrastructure and realize business value.

David Natsvlishvili

Data Scientist, Developer, Full Stack Developer, +4

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Hi, I’m David Natsvlishvili, an AI engineer with 16 years of experience designing, scaling, and deploying machine learning and generative AI systems across defense, enterprise, and global consumer platforms. I thrive at the intersection of research and engineering, turning cutting-edge ideas into production-ready solutions that drive measurable impact. Currently a Senior AI Engineer at Meta, I lead large-scale initiatives in LLaMA, RAG, and multimodal embeddings, and I develop PyTorch-based distributed training pipelines. I’ve built internal AI platforms that enable billions of daily predictions and created GenAI tools that let product teams experiment with LLMs in production workflows. Previously at Sutherland, I built predictive analytics and NLP solutions for enterprise clients in finance and telecom, deploying fraud detection, churn prediction, and intent classification systems. I started my career at Leidos as a Data Scientist, applying statistical modeling and anomaly detection to defense programs while creating ETL pipelines and dashboards for analysts. I’m passionate about bridging AI research and engineering execution and mentoring teams to scale infrastructure and realize business value.…Hi, I’m David Natsvlishvili, an AI engineer with 16 years of experience designing, scaling, and deploying machine learning and generative AI systems across defense, enterprise, and global consumer platforms. I thrive at the intersection of research and engineering, turning cutting-edge ideas into production-ready solutions that drive measurable impact. Currently a Senior AI Engineer at Meta, I lead large-scale initiatives in LLaMA, RAG, and multimodal embeddings, and I develop PyTorch-based distributed training pipelines. I’ve built internal AI platforms that enable billions of daily predictions and created GenAI tools that let product teams experiment with LLMs in production workflows. Previously at Sutherland, I built predictive analytics and NLP solutions for enterprise clients in finance and telecom, deploying fraud detection, churn prediction, and intent classification systems. I started my career at Leidos as a Data Scientist, applying statistical modeling and anomaly detection to defense programs while creating ETL pipelines and dashboards for analysts. I’m passionate about bridging AI research and engineering execution and mentoring teams to scale infrastructure and realize business value.

Available to hire

Hi, I’m David Natsvlishvili, an AI engineer with 16 years of experience designing, scaling, and deploying machine learning and generative AI systems across defense, enterprise, and global consumer platforms. I thrive at the intersection of research and engineering, turning cutting-edge ideas into production-ready solutions that drive measurable impact.

Currently a Senior AI Engineer at Meta, I lead large-scale initiatives in LLaMA, RAG, and multimodal embeddings, and I develop PyTorch-based distributed training pipelines. I’ve built internal AI platforms that enable billions of daily predictions and created GenAI tools that let product teams experiment with LLMs in production workflows. Previously at Sutherland, I built predictive analytics and NLP solutions for enterprise clients in finance and telecom, deploying fraud detection, churn prediction, and intent classification systems. I started my career at Leidos as a Data Scientist, applying statistical modeling and anomaly detection to defense programs while creating ETL pipelines and dashboards for analysts. I’m passionate about bridging AI research and engineering execution and mentoring teams to scale infrastructure and realize business value.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Intermediate

Language

English

Fluent

Work Experience

Senior AI Engineer at Meta

April 1, 2019 - November 6, 2025

Drove applied AI initiatives within FAIR and product engineering, focusing on PyTorch-based large-scale model training and deployment to support billions of daily predictions. Built a retrieval-augmented generation (RAG) platform on FAISS + PyTorch Lightning, powering internal semantic search across Meta's massive knowledge bases. Prototyped and shipped a GenAI experimentation tool using LLaMA with a React + GraphQL frontend and Python microservices backend. Improved distributed training and inference workflows on Kubernetes and Ray, making large-scale PyTorch jobs more reliable and faster to ship. Partnered with infra and product teams to scale multimodal embeddings (vision + text) for recommendation and ranking systems. Established MLOps best practices across teams — drift detection, MLflow-based experiment tracking, and custom Grafana dashboards for production LLMs.

Machine Learning Engineer at Sutherland

April 1, 2019 - April 1, 2019

Designed and implemented end-to-end ML solutions for global enterprise clients in finance, telecom, and customer support, with a focus on predictive analytics, NLP, and anomaly detection. Developed and deployed text classification, sentiment analysis, and intent detection models using scikit-learn, spaCy, and TensorFlow, significantly reducing manual case handling. Built fraud detection and churn prediction systems leveraging time-series forecasting, GLMs, and gradient boosting — improved detection accuracy and reduced false positives. Partnered with engineering teams to productionize ML models through REST APIs, Java-based services, and SQL pipelines, ensuring seamless integration with client applications. Established feature engineering and evaluation frameworks (cross-validation, cohort analysis, residual tracking) to improve model robustness and reliability in production.

Data Scientist at Leidos

February 1, 2012 - February 1, 2012

Developed statistical and machine learning models for defense and government programs, focusing on predictive analytics, anomaly detection, and risk assessment. Designed and maintained ETL pipelines in Python and SQL to consolidate and process large, heterogeneous datasets for downstream analysis. Applied classical ML techniques (logistic regression, clustering, ensemble methods) to improve intelligence analysis and operational decision-making. Built interactive visualization and reporting tools using C#/.NET, enabling analysts to explore model outputs and trends in real time. Partnered with engineering teams to integrate models into production workflows, balancing accuracy, interpretability, and computational efficiency.

Machine Learning Engineer at Sutherland

April 30, 2019 - April 30, 2019

Designed and implemented end-to-end ML solutions for global enterprise clients in finance, telecom, and customer support, focusing on predictive analytics, NLP, and anomaly detection. Developed and deployed text classification, sentiment analysis, and intent detection models using scikit-learn, spaCy, and TensorFlow; significantly reduced manual case handling. Built fraud detection and churn prediction systems leveraging time-series forecasting, GLMs, and gradient boosting, improving detection accuracy and reducing false positives. Partnered with engineering teams to productionize ML models through REST APIs, Java-based services, and SQL pipelines, ensuring seamless integration with client applications. Established feature engineering and evaluation frameworks to improve robustness and reliability in production.

Data Scientist at Leidos

February 29, 2012 - February 29, 2012

Senior AI Engineer at Meta

April 1, 2019 - November 13, 2025

Drove applied AI initiatives within FAIR and product engineering, focusing on PyTorch-based large-scale model training and deployment to support billions of daily predictions. Built a retrieval-augmented generation (RAG) platform on top of FAISS + PyTorch Lightning, powering internal semantic search across Meta’s massive knowledge bases. Prototyped and shipped a GenAI experimentation tool using LLaMA models with a React + GraphQL frontend and Python microservices backend. Improved distributed training and inference workflows on Kubernetes and Ray. Partnered with infra and product teams to scale multimodal embeddings (vision + text) for recommendation and ranking. Established MLOps practices across teams, including drift detection, MLflow-based experiment tracking, and production dashboards. Mentored engineers and shared best practices in internal PyTorch guilds and AI infra forums.