# Nitisha Sree Venkatesan

> ML Engineer turning petabyte-scale data into production AI | LLMs · Recommendation Systems · MLOps

Location: Dallas, Texas, United States
Profile: https://flows.cv/nitisha

Machine Learning Engineer with 4+ years of experience building and deploying scalable AI systems across consumer tech and SaaS, currently working at Meta. Specialized in LLM fine-tuning, transformers, and end-to-end MLOps, with hands-on expertise in AWS, Kubernetes, and Spark. Experienced in developing real-time recommendation systems, multimodal AI solutions, and optimizing models for performance and cost efficiency. Focused on delivering production-ready ML solutions that drive business impact by collaborating closely with product, data science, and infrastructure teams.

## Work Experience
### Machine Learning Engineer @ Meta
Jan 2024 – Present
At Meta, I led end-to-end AI systems powering personalization at a massive scale fine-tuning LLMs (LLaMA 2, Mistral) for multimodal summarization, building real-time recommendation engines with BERT & reinforcement learning, and engineering petabyte-scale data pipelines using PyTorch, Spark, and TensorFlow. On the infrastructure side, I automated CI/CD workflows via Kubernetes, FBLearner, and PyTorch Serve — cutting deployment cycles from weeks to days while maintaining 99.9% uptime. The result? Measurable lifts in ad relevance, feature extraction efficiency, and user engagement all while reducing compute costs through model pruning and quantization. From model to production, I owned the full stack.

### Machine Learning Engineer @ Freshworks
Jan 2020 – Jan 2023 | India
At Freshworks, I built the ML backbone that made SaaS smarter designing and deploying end-to-end models using XGBoost and deep learning for lead scoring, sentiment analysis, and automated ticket classification, directly boosting agent productivity and automation coverage. I architected the full ML infrastructure on AWS (SageMaker, Lambda, ECS), built large-scale feature engineering pipelines with Python and PySpark, and shipped production-ready APIs via FastAPI and Docker cutting inference latency and enabling clean microservices integration. Beyond building, I put in place monitoring and drift-detection frameworks to keep models reliable long after deployment. All of it delivered within Agile teams, aligned tightly to product goals and business outcomes. End-to-end ownership, zero hand-waving.


## Education
### Master's degree in Business Analytics And Artificial Intelligence
The University of Texas at Dallas


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

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